Biomarkers for Pre-Diabetes, Cardiovascular Diseases, and Other Metabolic-Syndrome Related Disorders and Methods Using the Same

ABSTRACT

Biomarkers relating to insulin resistance, pre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, and cardiomyopathy are provided, as well as methods for using such biomarkers as biomarkers for insulin resistance, pre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, and cardiomyopathy. In addition, methods for modulating the respective disorders or conditions of a subject are also provided. Also provided are suites of small molecule entities as biomarkers for insulin resistance, pre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, and cardiomyopathy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/966,475, filed Aug. 14, 2013, which is a divisional of U.S. patentapplication Ser. No. 13/457,620, filed Apr. 27, 2012, which is adivisional of U.S. patent application Ser. No. 12/218,980, filed Jul.17, 2008, which claims the benefit of U.S. Provisional Application No.60/950,286, filed Jul. 17, 2007, and U.S. Provisional Application No.61/037,628, filed Mar. 18, 2008, the entireties of which are herebyincorporated by reference herein.

FIELD

The invention generally relates to biomarkers, methods for identifyingbiomarkers correlated to insulin resistance, cardiovascular disease, andmetabolic-syndrome-related disorders and methods based on the samebiomarkers.

BACKGROUND

Diabetes is classified as either type 1 (early onset) or type 2 (adultonset), with type 2 comprising 90-95% of the cases of diabetes. Diabetesis the final stage in a disease process that begins to affectindividuals long before the diagnosis of diabetes is made. Type 2diabetes develops over 10 to 20 years and results from an impairedability to utilize glucose (glucose utilization) due to impairedsensitivity to insulin (insulin resistance).

In pre-diabetes, insulin becomes less effective at helping tissuesmetabolize glucose. Pre-diabetics may be detectable as early as 20 yearsbefore diabetic symptoms become evident. Studies have shown thatalthough patients show very few symptoms, long-term physiological damageis already occurring at this stage. Up to 60% of these individuals willprogress to type 2 diabetes within 10 years.

The American Diabetes Association (ADA) has recommended routinescreening to detect patients with pre-diabetes. Current screeningmethods for pre-diabetes include the fasting plasma glucose (FPG) test,the oral glucose tolerance test (OGTT), the fasting insulin test and thehyperinsulinemic euglycemic clamp (HI clamp). The first two tests areused clinically whereas the latter two tests are used extensively inresearch but rarely in the clinic. In addition, mathematical means(e.g., HOMA, QUICKI) that consider the fasting glucose and insulinlevels together have been proposed. However, normal plasma insulinconcentrations vary considerably between individuals as well as withinan individual throughout the day. Further, these methods suffer fromvariability and methodological differences between laboratories and donot correlate rigorously with glucose clamp studies.

Worldwide, an estimated 194 million adults have type 2 diabetes and thisnumber is expected to increase to 333 million by 2025, largely due tothe epidemic of obesity in westernized societies. In the United States,it is estimated that over 54 million adults are pre-diabetic, dependingon the level of insulin resistance. There are approximately 1.5 millionnew cases of type 2 diabetes a year in the United States. The annual UShealthcare cost for diabetes is estimated at $174 billion. This figurehas risen more than 32% since 2002. In industrialized countries such asthe U.S., about 25% of medical expenditures treat glycemic control, 50%is associated with general medical care associated with diabetes, andthe remaining 25% of the costs go to treat long-term complications,primarily cardiovascular disease. Considering the distribution of thehealthcare costs and the fact that insulin resistance is a direct causalfactor in cardiovascular disease and diabetes progression, it is nosurprise that cardiovascular disease accounts for 70-80% of themortality observed for diabetic patients. Detecting and preventing type2 diabetes has become a major health care priority.

Diabetes may also lead to the development of other diseases orconditions, or is a risk factor in the development of conditions such asMetabolic Syndrome and cardiovascular diseases. Metabolic Syndrome isthe clustering of a set of risk factors in an individual. According tothe American Heart Association these risk factors include: abdominalobesity, decreased ability to properly process glucose (insulinresistance or glucose intolerance), dyslipidemia (high triglycerides,high LDL, low HDL cholesterol), hypertension, prothrombotic state (highfibrinogen or plasminogen activator inhibitor-1 in the blood) andproinflammatory state (elevated C-reactive protein in the blood).Metabolic Syndrome is also known as syndrome X, insulin resistancesyndrome, obesity syndrome, dysmetabolic syndrome and Reaven's syndrome.Patients diagnosed with Metabolic Syndrome are at an increased risk ofdeveloping diabetes, cardiac and vascular disease. It is estimated that,in the United States, 20% of the adults (>50 million people) havemetabolic syndrome. While it can affect anyone at any age, the incidenceincreases with increasing age and in individuals who are inactive, andsignificantly overweight, especially with excess abdominal fat.

Type 2 diabetes is the most common form of diabetes in the UnitedStates. According to the American Diabetes Foundation over 90% of the USdiabetics suffer from Type 2 diabetes. Individuals with Type 2 diabeteshave a combination of increased insulin resistance and decreased insulinsecretion that combine to cause hyperglycemia. Most persons with Type 2diabetes have Metabolic Syndrome.

The diagnosis for Metabolic Syndrome is based upon the clustering ofthree or more of the risk factors in an individual. There are nowell-accepted criteria for diagnosing the metabolic syndrome. Thecriteria proposed by the National Cholesterol Education Program (NCEP)Adult Treatment Panel III (ATP III), with minor modifications, arecurrently recommended and widely used.

The American Heart Association and the National Heart, Lung, and BloodInstitute recommend that the metabolic syndrome be identified as thepresence of three or more of these components: increased waistcircumference (Men—equal to or greater than 40 inches (102 cm),Women—equal to or greater than 35 inches (88 cm); elevated triglycerides(equal to or greater than 150 mg/dL); reduced HDL (“good”) cholesterol(Men—less than 40 mg/dL, Women—less than 50 mg/dL); elevated bloodpressure (equal to or greater than 130/85 mm Hg); elevated fastingglucose (equal to or greater than 100 mg/dL).

Type 2 diabetes develops slowly and often people first learn they havetype 2 diabetes through blood tests done for another condition or aspart of a routine exam. In some cases, type 2 diabetes may not bedetected before damage to eyes, kidneys or other organs has occurred. Aneed exists for an objective, biochemical evaluation (e.g. lab test)that can be administered by a primary care provider to identifyindividuals that are at risk of developing Metabolic Syndrome or Type 2diabetes.

Newer, more innovative molecular diagnostics that reflect the mechanismsof the patho-physiological progression to pre-diabetes and diabetes areneeded because the prevalence of pre-diabetes and diabetes is increasingin global epidemic proportions. Mirroring the obesity epidemic,pre-diabetes and diabetes are largely preventable but are frequentlyundiagnosed or diagnosed too late due to the asymptomatic nature of theprogression to clinical disease.

Therefore there is an unmet need for diagnostic biomarkers and teststhat can identify pre-diabetics at risk of developing type 2 diabetesand to determine the risk of disease progression in subjects withinsulin resistance. Insulin resistance biomarkers and diagnostic testscan better identify and determine the risk of diabetes development in apre-diabetic subject, can monitor disease development and progressionand/or regression, can allow new therapeutic treatments to be developedand can be used to test therapeutic agents for efficacy on reversingpre-diabetes and/or preventing diabetes. Further, a need exists fordiagnostic biomarkers to more effectively assess the efficacy and safetyof pre-diabetic and diabetic therapeutic candidates.

SUMMARY OF THE INVENTION

In one embodiment, the present disclosure provides a method ofdiagnosing insulin resistance in a subject, the method comprisinganalyzing a biological sample from a subject to determine the level(s)of one or more biomarkers for insulin resistance in the sample, wherethe one or more biomarkers are selected from one or more biomarkerslisted in Tables 4, 5, 6, 7, 8, 9A, 9B, 27, 28, 29 and combinationsthereof; and comparing the level(s) of the one or more biomarkers in thesample to insulin resistance-positive and/or insulin resistance-negativereference levels of the one or more biomarkers in order to diagnosewhether the subject is insulin resistant.

In another embodiment, the present disclosure provides a method ofpredicting the glucose disposal rate in a subject, the methodcomprising, analyzing a biological sample from a subject to determinethe level(s) of one or more biomarkers for insulin resistance in thesample, where the one or more biomarkers are selected from one or morebiomarkers listed in Tables 4, 5, 6, 7, 8, 9A, 9B, and combinationsthereof; and comparing the level(s) of the one or more biomarkers in thesample to glucose disposal reference levels of the one or morebiomarkers in order to predict the glucosal disposal rate in thesubject.

The disclosure also provides a method of classifying a subject accordingto glucose tolerance from normal glucose tolerance (NGT), impairedfasting glucose tolerance (IFG), or impaired glucose tolerance (IGT), totype-2 diabetes, the method comprising, analyzing a biological samplefrom a subject to determine the level(s) of one or more biomarkers forglucose tolerance in the sample, where the one or more biomarkers areselected from one or more biomarkers listed in Tables 4, 5, 6, 7, 8, 9A,9B, and combinations thereof; and comparing the level(s) of the one ormore biomarkers in the sample to glucose tolerance reference levels ofthe one or more biomarkers in order to classify the subject as havingNGT, IFG, IGT, or diabetic.

Further provided is a method of determining susceptibility of a subjectto developing type-2 diabetes, the method comprising, analyzing abiological sample from a subject to determine the level(s) of one ormore biomarkers for pre-diabetes in the sample, where the one or morebiomarkers are selected from one or more biomarkers listed in Tables 4,5, 6, 7, 8, 9A, 9B, and combinations thereof; and comparing the level(s)of the one or more biomarkers in the sample to diabetes-positive and/ordiabetes-negative reference levels of the one or more biomarkers inorder to diagnose whether the subject is susceptible to developingtype-2 diabetes.

The present disclosure also provides a method of determining an insulinresistance score in a subject, the method comprising, analyzing abiological sample from a subject to determine the level(s) of one ormore biomarkers in the sample, where the one or more biomarkers areselected from one or more biomarkers listed in Tables 4, 5, 6, 7, 8, 9A,9B, and combinations thereof; and comparing the level(s) of the one ormore biomarkers in the sample to insulin resistance reference levels ofthe one or more biomarkers in order to determine an insulin resistancescore for the subject.

In another embodiment, the present disclosure provides a method ofmonitoring the progression or regression of pre-diabetes in a subject,the method comprising, analyzing a biological sample from a subject todetermine the level(s) of one or more biomarkers for pre-diabetes in thesample, where the one or more biomarkers are selected from one or morebiomarkers listed in Tables 4, 5, 6, 7, 8, 9A, 9B, and combinationsthereof; and comparing the level(s) of the one or more biomarkers in thesample to pre-diabetes progression and/or pre-diabetes-regressionreference levels of the one or more biomarkers in order to monitor theprogression or regression of pre-diabetes in a subject.

In yet another embodiment, the present disclosure provides a method ofmonitoring the efficacy of insulin resistance treatment, the methodcomprising: analyzing a first biological sample from a subject todetermine the level(s) of one or more biomarkers for pre-diabetes, thefirst sample obtained from the subject at a first time point wherein theone or more biomarkers are selected from one or more biomarkers listedin Tables 4, 5, 6, 7, and 8, and combinations thereof; treating thesubject for insulin resistance; analyzing a second biological samplefrom the subject to determine the level(s) of the one or morebiomarkers, the second sample obtained from the subject at a second timepoint after treatment; comparing the level(s) of one or more biomarkersin the first sample to the level(s) of the one or more biomarkers in thesecond sample to assess the efficacy of the treatment for treatinginsulin resistance.

The present disclosure further provides a method of diagnosing whether asubject has metabolic syndrome, the method comprising, analyzing abiological sample from a subject to determine the level(s) of one ormore biomarkers for metabolic syndrome in the sample, where the one ormore biomarkers are selected from one or more biomarkers listed inTables 12 and 13, analyzing the biological sample to determine thelevel(s) of one or more biomarkers for glucose disposal, obesity, and/orcardiovascular disease, wherein the one or more biomarkers for glucosedisposal, obesity, and/or cardiovascular disease are selected from oneor more biomarkers identified in Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15,16, 17, 21, 22, 23, 25, 26, 27, 28, and combinations thereof; andcomparing the level(s) of the one or more biomarkers in the sample tothe respective disorder-positive and/or disorder-negative referencelevels of the one or more biomarkers in order to diagnose whether thesubject has the metabolic syndrome.

In another embodiment, the present disclosure provides a method ofdiagnosing a cardiovascular disease in a subject, the method comprising,analyzing a biological sample from a subject to determine the level(s)of one or more biomarkers for a cardiovascular disease in the sample,where the one or more biomarkers are selected from one or morebiomarkers listed in Tables 14, 15, 16, 17, 21, 22, 23, 25, andcombinations thereof; and comparing the level(s) of the one or morebiomarkers in the sample to disease-positive and/or disease-negativereference levels of the one or more biomarkers in order to diagnosewhether the subject has cardiomyopathy or atherosclerosis.

The disclosure further provides a method for determining whether asubject is predisposed to becoming obese, the method comprising:analyzing a biological sample from a subject to determine the level(s)of one or more biomarkers for obesity in the sample, where the one ormore biomarkers are selected from one or more biomarkers listed in Table26; and comparing the level(s) of the one or more biomarkers in thesample to obesity-positive and/or obesity-negative and/or lean-positiveand/or lean-negative reference levels of the one or more biomarkers inorder to determine whether the subject is susceptible to obesity.

In yet a further embodiment, the disclosure provides a method fordetermining whether a therapeutic agent is capable of inducing weightgain in a subject, the method comprising: analyzing a biological samplefrom a subject receiving a therapeutic agent to determine the level(s)of one or more biomarkers for obesity in the sample, where the one ormore biomarkers are selected from one or more biomarkers listed in Table26; and comparing the level(s) of the one or more biomarkers in thesample to obesity-positive and/or obesity-negative and/or lean-positiveand/or lean-negative reference levels of the one or more biomarkers inorder to determine whether the subject is susceptible to gaining weight.

The present disclosure also provides a method for predicting a subject'sresponse to a course of treatment for pre-diabetes or diabetes, themethod comprising: analyzing a biological sample from a subject todetermine the level(s) of one or more biomarkers for pre-diabetes, wherethe one or more biomarkers are selected from one or more biomarkerslisted in Table 27; comparing the level(s) of one or more biomarkers inthe sample to treatment-positive and/or treatment-negative referencelevels of the one or more biomarkers to predict whether the subject islikely to respond to a course of treatment.

The disclosure also provides a method for monitoring a subject'sresponse to a treatment for pre-diabetes or diabetes, the methodcomprising: analyzing a first biological sample from a subject todetermine the level(s) of one or more biomarkers for pre-diabetes, thefirst sample obtained from the subject at a first time point where theone or more biomarkers are selected from one or more biomarkers listedin Table 28; administering the composition to the subject; analyzing asecond biological sample from the subject to determine the level(s) ofthe one or more biomarkers, the second sample obtained from the subjectat a second time point after administration of the composition;comparing the level(s) of one or more biomarkers in the first sample tothe level(s) of the one or more biomarkers in the second sample toassess the efficacy of the composition for treating pre-diabetes ordiabetes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a graph showing the mean R² values of Rd correlation asa function of the number of metabolites. As the number of compoundsincreases (from right to left), the r-square value for Rd correlation(Y) increases until it reaches an optimal number (n<30), indicating thatvariable selection is more or less stable for the approximately first 30variables.

FIG. 2 provides a graph showing the mean R² values of Rd correlation asa function of the number of metabolites. As the number of compoundsincreases (from right to left), test error for Rd correlation (Y)decreases until it reach an optimal number (n<30), indicating thatvariable selection is more or less stable for the approximately first 30variables.

FIG. 3 provides a graph showing the mean R-square values (Y-axis) of Rdcorrelation as a function of the number of metabolites (X-axis).

FIG. 4 provides a graph showing the mean test error values (Y-axis) ofRd correlation as a function of the number of metabolites (X-axis).

FIG. 5 provides a schematic example of a report describing propheticresults of an algorithm using insulin resistance biomarkers to determinea subject's level of insulin resistance that is reported as an “IRScore” and containing additional clinical information (e.g. BMI,demographic information).

FIG. 6 provides a schematic illustration comparing the use of biomarkersfor patient stratification according to the level of insulin resistanceand the use of biomarkers for patient risk stratification for theprogression of insulin resistance.

FIG. 7 provides a Random Forest Analysis Importance plot of oneembodiment of metabolites that are useful biomarkers for predictingglucose disposal.

FIG. 8 provides a Random Forest Analysis Importance Plot of oneembodiment of serum metabolites that are useful biomarkers forpredicting metabolic syndrome.

FIG. 9 provides a Random Forest Analysis Importance Plot of oneembodiment of plasma metabolites that are useful biomarkers forpredicting metabolic syndrome.

FIG. 10 provides a Random Forest Analysis Importance Plots ofembodiments of metabolites from plasma that are useful biomarkers forpredicting atherosclerosis at early (initiation) (FIG. 10A), mid (FIG.10B), later (FIG. 10C), or all (FIG. 10D) stages of the disease.

FIG. 11 provides a Random Forest Analysis Importance Plots ofembodiments of metabolites from aorta tissue that are useful biomarkersfor predicting atherosclerosis at early (initiation) (FIG. 11A), mid(FIG. 11B), later (FIG. 11C), or all (FIG. 11D) stages of the disease.

FIG. 12 provides a Random Forest Analysis Importance Plots ofembodiments of metabolites from liver tissue that are useful biomarkersfor predicting atherosclerosis at early (initiation) (FIG. 12A), mid(FIG. 12B), later (FIG. 12C), or all (FIG. 12D) stages of the disease.

FIG. 13 provides an example of plasma levels of cholesterol inatherosclerosis subjects and control subjects at different ages.

FIG. 14 provides an example of plasma levels of docosahexaenoic acid inatherosclerosis subjects and control subjects at different ages.

FIG. 15 provides an example of plasma levels of Metabolite-7888 inatherosclerosis subjects and control subjects at different ages.

FIG. 16 provides an example of plasma levels of Metabolite-X8403 inatherosclerosis subjects and control subjects at different ages.

FIG. 17 provides an example of plasma levels of Metabolite-X1834 inatherosclerosis subjects and control subjects at different ages.

FIG. 18 provides an example of plasma levels of p-cresol-sulfate inatherosclerosis subjects and control subjects at different ages.

FIG. 19 provides an example of plasma levels of Metabolite-4887 inatherosclerosis subjects and control subjects at different ages.

FIG. 20 provides an example of recursive partitioning of DCM biomarkermetabolites.

FIG. 21 provides an example of model validation using plasma frommetabolic syndrome and healthy subjects.

FIG. 22 provides an example of a model validation using serum frommetabolic syndrome and healthy subjects.

FIG. 23 provides an example of a regression analysis showing thepredictive power of the ten models combined on the glucose disposal rate(Rd).

FIG. 24 provides and illustration of the inter-relationships of thevarious risk factors for metabolic syndrome.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to biomarkers of pre-diabetes (e.g.impaired glucose tolerance, impaired fasting glucose tolerance, insulinresistance) and type-2 diabetes; methods for diagnosis of pre-diabetesand type 2 diabetes; methods of determining predisposition topre-diabetes and type 2 diabetes; methods of monitoringprogression/regression of pre-diabetes and type 2 diabetes; methods ofassessing efficacy of compositions for treating pre-diabetes and type 2diabetes; methods of screening compositions for activity in modulatingbiomarkers of pre-diabetes and type 2 diabetes; methods of treatingpre-diabetes and type 2 diabetes; as well as other methods based onbiomarkers of pre-diabetes and type 2 diabetes.

Current blood tests for insulin resistance perform poorly for earlydetection of insulin resistance or involve significant medicalprocedures.

Using metabolomic analysis, panels of metabolites that can be used in asimple blood test to predict insulin resistance as measured by the “goldstandard” of hyperinsulinemic euglycemic clamps in at least twoindependent cohorts of subjects were discovered.

Independent studies were carried out to identify a set of biomarkersthat when used with a polynomic algorithm will enable the earlydetection of changes in insulin resistance in a subject. The instantinvention provides the subject with a score indicating the level ofinsulin resistance (“IR Score”) of the subject. The score can be basedupon clinically significant changed reference level for a biomarkerand/or combination of biomarkers. The reference level can be derivedfrom an algorithm or computed from indices for impaired glucosetolerance and can be presented in a report as shown in FIG. 5. The IRScore places the subject in the range of insulin resistance from normalto high. Disease progression or remission can be monitored by periodicdetermination and monitoring of the IR Score. Response to therapeuticintervention can be determined by monitoring the IR Score. The IR Scorecan also be used to evaluate drug efficacy.

The present invention also relates to biomarkers of metabolic syndromeand cardiovascular diseases, such as atherosclerosis and cardiomyopathy;methods for diagnosis of such diseases and syndromes; methods ofdetermining predisposition to such diseases and syndromes; methods ofmonitoring progression/regression of such diseases and syndromes;methods of assessing efficacy of compositions for treating such diseasesand syndromes; methods of screening compositions for activity inmodulating biomarkers of such diseases and syndromes; methods oftreating such diseases and syndromes; as well as other methods based onbiomarkers of such diseases and syndromes.

Prior to describing this invention in further detail, however, thefollowing terms will first be defined.

DEFINITIONS

“Biomarker” means a compound, preferably a metabolite, that isdifferentially present (i.e., increased or decreased) in a biologicalsample from a subject or a group of subjects having a first phenotype(e.g., having a disease) as compared to a biological sample from asubject or group of subjects having a second phenotype (e.g., not havingthe disease). A biomarker may be differentially present at any level,but is generally present at a level that is increased by at least 5%, byat least 10%, by at least 15%, by at least 20%, by at least 25%, by atleast 30%, by at least 35%, by at least 40%, by at least 45%, by atleast 50%, by at least 55%, by at least 60%, by at least 65%, by atleast 70%, by at least 75%, by at least 80%, by at least 85%, by atleast 90%, by at least 95%, by at least 100%, by at least 110%, by atleast 120%, by at least 130%, by at least 140%, by at least 150%, ormore; or is generally present at a level that is decreased by at least5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%,by at least 30%, by at least 35%, by at least 40%, by at least 45%, byat least 50%, by at least 55%, by at least 60%, by at least 65%, by atleast 70%, by at least 75%, by at least 80%, by at least 85%, by atleast 90%, by at least 95%, or by 100% (i.e., absent). A biomarker ispreferably differentially present at a level that is statisticallysignificant (e.g., a p-value less than 0.05 and/or a q-value of lessthan 0.10 as determined using either Welch's T-test or Wilcoxon'srank-sum Test). Alternatively, the biomarkers demonstrate a correlationwith pre-diabetes, or particular levels of pre-diabetes. The range ofpossible correlations is between negative (−) 1 and positive (+) 1. Aresult of negative (−) 1 means a perfect negative correlation and apositive (+) 1 means a perfect positive correlation, and 0 means nocorrelation at all. A “substantial positive correlation” refers to abiomarker having a correlation from +0.25 to +1.0 with a disorder orwith a clinical measurement (e.g., Rd), while a “substantial negativecorrelation” refers to a correlation from −0.25 to −1.0 with a givendisorder or clinical measurement. A “significant positive correlation”refers to a biomarker having a correlation of from +0.5 to +1.0 with agiven disorder or clinical measurement (e.g., Rd), while a “significantnegative correlation” refers to a correlation to a disorder of from −0.5to −1.0 with a given disorder or clinical measurement.

The “level” of one or more biomarkers means the absolute or relativeamount or concentration of the biomarker in the sample.

“Sample” or “biological sample” or “specimen” means biological materialisolated from a subject. The biological sample may contain anybiological material suitable for detecting the desired biomarkers, andmay comprise cellular and/or non-cellular material from the subject. Thesample can be isolated from any suitable biological tissue or fluid suchas, for example, adipose tissue, aortic tissue, liver tissue, blood,blood plasma, serum, or urine.

“Subject” means any animal, but is preferably a mammal, such as, forexample, a human, monkey, non-human primate, rat, mouse, cow, dog, cat,pig, horse, or rabbit.

A “reference level” of a biomarker means a level of the biomarker thatis indicative of a particular disease state, phenotype, or lack thereof,as well as combinations of disease states, phenotypes, or lack thereof.A “positive” reference level of a biomarker means a level that isindicative of a particular disease state or phenotype. A “negative”reference level of a biomarker means a level that is indicative of alack of a particular disease state or phenotype. For example, a“pre-diabetes-positive reference level” of a biomarker means a level ofa biomarker that is indicative of a positive diagnosis of pre-diabetesin a subject, and a “pre-diabetes-negative reference level” of abiomarker means a level of a biomarker that is indicative of a negativediagnosis of pre-diabetes in a subject. As another example, a“pre-diabetes-progression-positive reference level” of a biomarker meansa level of a biomarker that is indicative of progression of thepre-diabetes in a subject, and a “pre-diabetes-regression-positivereference level” of a biomarker means a level of a biomarker that isindicative of regression of the pre-diabetes. A “reference level” of abiomarker may be an absolute or relative amount or concentration of thebiomarker, a presence or absence of the biomarker, a range of amount orconcentration of the biomarker, a minimum and/or maximum amount orconcentration of the biomarker, a mean amount or concentration of thebiomarker, and/or a median amount or concentration of the biomarker;and, in addition, “reference levels” of combinations of biomarkers mayalso be ratios of absolute or relative amounts or concentrations of twoor more biomarkers with respect to each other. Appropriate positive andnegative reference levels of biomarkers for a particular disease state,phenotype, or lack thereof may be determined by measuring levels ofdesired biomarkers in one or more appropriate subjects, and suchreference levels may be tailored to specific populations of subjects(e.g., a reference level may be age-matched so that comparisons may bemade between biomarker levels in samples from subjects of a certain ageand reference levels for a particular disease state, phenotype, or lackthereof in a certain age group). Such reference levels may also betailored to specific techniques that are used to measure levels ofbiomarkers in biological samples (e.g., LC-MS, GC-MS, etc.), where thelevels of biomarkers may differ based on the specific technique that isused.

“Non-biomarker compound” means a compound that is not differentiallypresent in a biological sample from a subject or a group of subjectshaving a first phenotype (e.g., having a first disease) as compared to abiological sample from a subject or group of subjects having a secondphenotype (e.g., not having the first disease). Such non-biomarkercompounds may, however, be biomarkers in a biological sample from asubject or a group of subjects having a third phenotype (e.g., having asecond disease) as compared to the first phenotype (e.g., having thefirst disease) or the second phenotype (e.g., not having the firstdisease).

“Metabolite”, or “small molecule”, means organic and inorganic moleculeswhich are present in a cell. The term does not include largemacromolecules, such as large proteins (e.g., proteins with molecularweights over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or10,000), large nucleic acids (e.g., nucleic acids with molecular weightsof over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or10,000), or large polysaccharides (e.g., polysaccharides with amolecular weights of over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000,8,000, 9,000, or 10,000). The small molecules of the cell are generallyfound free in solution in the cytoplasm or in other organelles, such asthe mitochondria, where they form a pool of intermediates which can bemetabolized further or used to generate large molecules, calledmacromolecules. The term “small molecules” includes signaling moleculesand intermediates in the chemical reactions that transform energyderived from food into usable forms. Examples of small molecules includesugars, fatty acids, amino acids, nucleotides, intermediates formedduring cellular processes, and other small molecules found within thecell.

“Metabolic profile”, or “small molecule profile”, means a complete orpartial inventory of small molecules within a targeted cell, tissue,organ, organism, or fraction thereof (e.g., cellular compartment). Theinventory may include the quantity and/or type of small moleculespresent. The “small molecule profile” may be determined using a singletechnique or multiple different techniques.

“Metabolome” means all of the small molecules present in a givenorganism.

“Metabolic disorder” refers to disorders or diseases that result inperturbation of the normal physiological state of homeostasis due to analteration in metabolism (anabolism and/or catabolism). An alteration inmetabolism can result from an inability to break down (catabolize) asubstance that should be broken down (e.g. phenylalanine) and as aresult the substance and/or an intermediate substance builds up to toxiclevels, or from an inability to produce (anabolize) some essentialsubstance (e.g. insulin).

“Metabolic syndrome” refers to the concept of a clustering of metabolicrisk factors that come together in a single individual and lead to ahigh risk of developing diabetes and/or cardiovascular diseases. Themain features of metabolic syndrome include insulin resistance,hypertension (high blood pressure), cholesterol abnormalities,dyslipidemia, triglyceride abnormalities, an increased risk for clottingand excess body weight, especially in the abdomen, or obesity. Metabolicsyndrome is also known as syndrome X, insulin resistance syndrome,obesity syndrome, dysmetabolic syndrome and Reaven's syndrome. Theinter-relationships of the various risk factors for metabolic syndromeare illustrated in FIG. 24. The presence of three or more of the riskfactors in a single individual is indicative of metabolic syndrome. TheAmerican Heart Association suggests that metabolic syndrome be diagnosedby the presence of three or more of the following components: (1) anelevated waste circumference (men, equal to or greater than 40 inches(102 cm); women, equal to or greater than 35 inches (88 cm)); (2)elevated triglycerides (equal to or greater than 150 mg/dL); (3) reducedHigh Density Lipids or HDL (men, less than 40 mg/dL; women, less than 50mg/dL); (4) elevated blood pressure (equal to or greater than 130/85 mmHg); and (5) elevated fasting glucose (equal to or greater than 100mg/dL).

“Metabolic syndrome-related metabolic disorder” as used herein refers tometabolic syndrome as well as obesity, insulin resistance, type-2diabetes, atherosclerosis, and cardiomyopathy.

“Diabetes” refers to a group of metabolic diseases characterized by highblood sugar (glucose) levels which result from defects in insulinsecretion or action, or both.

“Type 2 diabetes” refers to one of the two major types of diabetes, thetype in which the beta cells of the pancreas produce insulin, at leastin the early stages of the disease, but the body is unable to use iteffectively because the cells of the body are resistant to the action ofinsulin. In later stages of the disease the beta cells may stopproducing insulin. Type 2 diabetes is also known as insulin-resistantdiabetes, non-insulin dependent diabetes and adult-onset diabetes.

“Pre-diabetes” refers to one or more early diabetic conditions includingimpaired glucose utilization, abnormal or impaired fasting glucoselevels, impaired glucose tolerance, impaired insulin sensitivity andinsulin resistance.

“Insulin resistance” refers to the condition when cells become resistantto the effects of insulin—a hormone that regulates the uptake of glucoseinto cells—or when the amount of insulin produced is insufficient tomaintain a normal glucose level. Cells are diminished in the ability torespond to the action of insulin in promoting the transport of the sugarglucose from blood into muscles and other tissues(i.e. sensitivity toinsulin decreases). Eventually, the pancreas produces far more insulinthan normal and the cells continue to be resistant. As long as enoughinsulin is produced to overcome this resistance, blood glucose levelsremain normal. Once the pancreas is no longer able to keep up, bloodglucose starts to rise, resulting in diabetes. Insulin resistance rangesfrom normal (insulin sensitive) to insulin resistant (IR).

“Insulin sensitivity” refers to the ability of cells to respond to theeffects of insulin to regulate the uptake and utilization of glucose.Insulin sensitivity ranges from normal to Insulin Resistant (IR).

The “IR Score” is a measure of insulin resistance based upon the insulinresistance biomarkers and algorithms of the instant invention that willallow the physician to place the patient on the spectrum of glucosetolerance, from normal to high.

“Glucose utilization” refers to the absorption of glucose from the bloodby muscle and fat cells and utilization of the sugar for cellularmetabolism. The uptake of glucose into cells is stimulated by insulin.

“Rd” refers to glucose disposal rate, a metric for glucose utilization.The rate at which glucose disappears from the blood (disposal rate) isan indication of the ability of the body to respond to insulin (i.e.insulin sensitivity). There are several methods to determine Rd and thehyperinsulinemic euglycemic clamp is regarded as the “gold standard”method. In this technique, while a fixed amount of insulin is infused,the blood glucose is “clamped” at a predetermined level by the titrationof a variable rate of glucose infusion. The underlying principle is thatupon reaching steady state, by definition, glucose disposal isequivalent to glucose appearance. During hyperinsulinemia, glucosedisposal (Rd) is primarily accounted for by glucose uptake into skeletalmuscle, and glucose appearance is equal to the sum of the exogenousglucose infusion rate plus the rate of hepatic glucose output (HGO). Therate of glucose infusion during the last 30 minutes of the testdetermines insulin sensitivity. If high levels (Rd=7.5 mg/min or higher)are required, the patient is insulin-sensitive. Very low levels (Rd=4.0mg/min or lower) indicate that the body is resistant to insulin action.Levels between 4.0 and 7.5 mg/min (Rd values between 4.0 mg/min and 7.5mg/min) are not definitive and suggest “impaired glucose tolerance,” anearly sign of insulin resistance.

“Impaired fasting glucose (IFG)” and “impaired glucose tolerance (IGT)”are the two clinical definitions of “pre-diabetes”. IFG is defined as afasting blood glucose concentration of 100-125 mg/dL. IGT is defined asa postprandial (after eating) blood glucose concentration of 140-199mg/dL. It is known that IFG and IGT do not always detect the samepre-diabetic populations. Between the two populations there isapproximately a 60% overlap observed. Fasting plasma glucose levels area more efficient means of inferring a patient's pancreatic function, orinsulin secretion, whereas postprandial glucose levels are morefrequently associated with inferring levels of insulin sensitivity orresistance. IGT is known to identify a greater percentage of thepre-diabetic population compared to IFG. The IFG condition is associatedwith lower insulin secretion, whereas the IGT condition is known to bestrongly associated with insulin resistance. Numerous studies have beencarried out that demonstrate that IGT individuals with normal FPG valuesare at increased risk for cardiovascular disease. Patients with normalFPG values may have abnormal postprandial glucose values and are oftenunaware of their risk for pre-diabetes, diabetes, and cardiovasculardisease.

“Fasting plasma glucose (FPG) test” is a simple test measuring bloodglucose levels after an 8 hour fast. According to the ADA, blood glucoseconcentration of 100-125 mg/dL is considered IFG and definespre-diabetes whereas ≧126 mg/dL defines diabetes. As stated by the ADA,FPG is the preferred test to diagnose diabetes and pre-diabetes due toits ease of use, patient acceptability, lower cost, and relativereproducibility. The weakness in the FPG test is that patients are quiteadvanced toward Type 2 Diabetes before fasting glucose levels change.

“Oral glucose tolerance test (OGTT)”, a dynamic measurement of glucose,is a postprandial measurement of a patient's blood glucose levels afteroral ingestion of a 75 g glucose drink. Traditional measurements includea fasting blood sample at the beginning of the test, a one hour timepoint blood sample, and a 2 hour time point blood sample. A patient'sblood glucose concentration at the 2 hour time point defines the levelof glucose tolerance: Normal glucose tolerance (NGT) ≦140 mg/dL bloodglucose; Impaired glucose tolerance (IGT)=140-199 mg/dL blood glucose;Diabetes ≧200 mg/dL blood glucose. As stated by the ADA, even though theOGTT is known to be more sensitive and specific at diagnosingpre-diabetes and diabetes, it is not recommended for routine clinicaluse because of its poor reproducibility and difficulty to perform inpractice.

“Fasting insulin test” measures the circulating mature form of insulinin plasma. The current definition of hyperinsulinemia is difficult dueto lack of standardization of insulin immunoassays, cross-reactivity toproinsulin forms, and no consensus on analytical requirements for theassays. Within-assay CVs range from 3.7%-39% and among-assay CVs rangefrom 12%-66%. Therefore, fasting insulin is not commonly measured in theclinical setting and is limited to the research setting.

The “hyperinsulinemic euglycemic clamp (HI clamp)” is consideredworldwide as the “gold standard” for measuring insulin resistance inpatients. It is performed in a research setting, requires insertion oftwo catheters into the patient and the patient must remain immobilizedfor up to six hours. The HI clamp involves creating steady-statehyperinsulinemia by insulin infusion, along with parallel glucoseinfusion in order to quantify the required amount of glucose to maintaineuglycemia (normal concentration of glucose in the blood; also callednormoglycemia). The result is a measure of the insulin-dependent glucosedisposal rate (Rd), measuring the peripheral uptake of glucose by themuscle (primarily) and adipose tissues. This rate of glucose uptake isnotated by M, whole body glucose metabolism by insulin action understeady state conditions. Therefore, a high M indicates high insulinsensitivity and a lower M value indicates reduced insulin sensitivity,i.e. insulin resistance. The HI clamp requires three trainedprofessionals to carry out the procedure, including simultaneousinfusions of insulin and glucose over 2-4 hours and frequent bloodsampling every 5 minutes for analysis of insulin and glucose levels. Dueto the high cost, complexity, and time required for the HI clamp, thisprocedure is strictly limited to the clinical research setting.

“Obesity” refers to a chronic condition defined by an excess amount bodyfat. The normal amount of body fat (expressed as percentage of bodyweight) is between 25-30% in women and 18-23% in men. Women with over30% body fat and men with over 25% body fat are considered obese.

“Body Mass Index, (or BMI)” refers to a calculation that uses the heightand weight of an individual to estimate the amount of the individual'sbody fat. Too much body fat (e.g. obesity) can lead to illnesses andother health problems. BMI is the measurement of choice for manyphysicians and researchers studying obesity. BMI is calculated using amathematical formula that takes into account both height and weight ofthe individual. BMI equals a person's weight in kilograms divided byheight in meters squared. (BMI=kg/m²). Subjects having a BMI less than19 are considered to be underweight, while those with a BMI of between19 and 25 are considered to be of normal weight, while a BMI of between25 to 29 are generally considered overweight, while individuals with aBMI of 30 or more are typically considered obese. Morbid obesity refersto a subject having a BMI of 40 or greater.

“Cardiovascular disease” refers to any disease of the heart or bloodvessels. Cardiovascular or heart disease includes but is not limited to,for example, angina, arrhythmia, coronary artery disease (CAD), coronaryheart disease, cardiomyopathy (including dilated cardiomyopathy,restrictive cardiomyopathy, arrhythmogenic right ventricularcardiomyopathy, and diabetic cardiomyopathy) heart attack (myocardialinfarction), heart failure, hypertrophic cardiomyopathy, mitralregurgitation, mitral valve prolapse, pulmonary stenosis, etc. Bloodvessel disease includes but is not limited to, for example, peripheralvascular disease, artery disease, carotid artery disease, deep veinthrombosis, venous diseases, atherosclerosis, etc.

I. Biomarkers

The biomarkers described herein were discovered using metabolomicprofiling techniques. Such metabolomic profiling techniques aredescribed in more detail in the Examples set forth below as well as inU.S. Pat. Nos. 7,005,255 and 7,329,489 and U.S. patent application Ser.No. 11/357,732 (Publication No. 2007/0026389), Ser. No. 11/301,077(Publication No. 2006/0134676), Ser. No. 11/301,078 (Publication No.2006/0134677), Ser. No. 11/301,079 (Publication No. 2006/0134678), andSer. No. 11/405,033 (Publication No. US 2007/0072203), the entirecontents of which are hereby incorporated herein by reference.

Generally, metabolic profiles may be determined for biological samplesfrom human subjects diagnosed with a condition such as pre-diabetes aswell as from one or more other groups of human subjects (e.g., healthycontrol subjects with normal glucose tolerance, subjects with impairedglucose tolerance, subjects with insulin resistance). The metabolicprofile for a pre-diabetes disorder may then be compared to themetabolic profile for biological samples from the one or more othergroups of subjects. The comparisons may be conducted using models oralgorithms, such as those described herein. Those moleculesdifferentially present, including those molecules differentially presentat a level that is statistically significant, in the metabolic profileof samples from subjects with a pre-diabetes disorder as compared toanother group (e.g., healthy control subjects not pre-diabetic) may beidentified as biomarkers to distinguish those groups.

Biomarkers for use in the methods disclosed herein may be obtained fromany source of biomarkers related to pre-diabetes and/or type-2 diabetes.Biomarkers for use in methods disclosed herein relating to pre-diabetesinclude those listed in Tables 4, 5, 6, 7, 8, 9A, 9B, 27, 28, 29, andcombinations and subsets thereof. In one embodiment, the biomarkersinclude those listed in Tables 4, 5, 6, 7, 8, 9A, 9B, 27, 28, andcombinations thereof. Additional biomarkers include those disclosed inU.S. Application No. 60/950,286, the entirety of which is herebyincorporated by reference in its entirety. In one aspect, the biomarkerscorrelate to insulin resistance.

Biomarkers for use in methods disclosed herein relating to metabolicsyndrome-related metabolic disorders include those listed in Tables 4,5, 6, 7, 8, 9A, 9B, 12, 13, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, 28,29, and combinations thereof. For example, biomarkers for use indistinguishing, or aiding in distinguishing, between subjects havingmetabolic syndrome and subjects not having metabolic syndrome includethose biomarkers identified in Tables 4, 5, 6, 7, 8, 9A, 9B, 12, 13, 14,15, 16, 17, 21, 22, 23, 25, 26, 27, 28, 29, and combinations thereof. Inone aspect, biomarkers for use in methods relating to metabolic syndromeinclude one or more of those listed in Tables 12 and 13. In anotheraspect biomarkers for use in methods relating to metabolic syndromeusing plasma samples obtained from a subject include one or more ofthose listed in Table 12. In a preferred aspect, the biomarkers for usein methods disclosed herein related to metabolic syndrome using plasmasamples obtained from a subject include one or more of the biomarkersN-acetylglycine, metabolite-6346, metabolite-8792, gamma-glu-leu,metabolite-4806, metabolite-3165, metabolite-7762, metabolite-3030,metabolite-5978, metabolite-3218, metabolite-2000, metabolite-5848,metabolite-3370, malic acid, metabolite-3843, metabolite-4275,metabolite-3094, metabolite-4167, metabolite-3073, aldosterone,metabolite-1320, metabolite-2185, phenylalanine, metabolite-2139,glutamic acid, alpha-tocopherol, metabolite-5767, metabolite-5346,metabolite-9855, and 1-octadecanol, and combinations thereof. In yetanother aspect, biomarkers for use in methods relating to metabolicsyndrome using serum samples obtained from a subject include one or moreof those listed in Table 13. In a preferred aspect, the biomarkers foruse in metabolic syndrome methods disclosed herein using serum samplesobtained from a subject include one or more of the biomarkersmetabolite-8792, metabolite-5767, metabolite-2139, metabolite-8402,metabolite-3073, phenylalanine, metabolite-4929, metabolite-3370,nonanate, N-acetylglycine, metabolite-5848, metabolite-3077,monopalmitin, dioctyl-phthalate, octadecanoic acid, cholesterol,metabolite-2608, metabolite-6272, metabolite-3012, D-glucose,metabolite-2986, metabolite-4275, metabolite-6268, tyrosine,metabolite-10683, metabolite-2000, alpha-tocopherol, metabolite-2469,xanthine, and metabolite-2039, and combinations thereof.

In another aspect, biomarkers for use in methods disclosed hereinrelating to metabolic syndrome may include the use of one or morebiomarkers listed in Tables 12 and/or 13 in combination with one or morebiomarkers in one or more of Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16,17, 21, 22, 23, 25, 26, 27, 28, or combinations thereof. For example,biomarkers for use in methods relating to metabolic syndrome may includeone or more biomarkers listed in Tables 12 and/or 13 in combination withone or more biomarkers associated with insulin resistance, such as thoselisted in Tables 4, 5, 6, 7, 8, 9A, 9B, 27, 28, or combinations thereof.

Biomarkers for use in methods disclosed herein relating to pre-diabeticor diabetic conditions, such as impaired insulin sensitivity, insulinresistance, or type-2 diabetes include one or more of those listed inTables 4, 5, 6, 7, 8, 9A, 9B, 27, 28, and combinations thereof. Suchbiomarkers allow subjects to be classified as insulin resistant, insulinimpaired, or insulin sensitive. In one aspect, biomarkers for use indistinguishing or aiding in distinguishing, between subjects havingimpaired insulin sensitivity from subjects not having impaired insulinsensitivity include one or more of those listed in Table 4, 5, 6, 7, 8,9A, and/or 9B. In another aspect, biomarkers for use in diagnosinginsulin resistance include one or more of those listed in Tables 4, 5,6, 7, 8, 9A, and/or 9B. In another example, biomarkers for use indistinguishing subjects having insulin resistance from subject nothaving insulin resistance include one or more of those listed in Tables4, 5, 6, 7, 8, 9A, and/or 9B. In another example, biomarkers for use incategorizing, or aiding in categorizing, a subject as having impairedfasting glucose levels or impaired glucose tolerance include one or moreof those listed in Tables 4, 5, 6, 7, 8, 9A, and/or 9B.

Biomarkers for use in methods disclosed herein relating toatherosclerosis include one or more of those listed in Tables 14, 15,16, and/or 17 and combinations thereof. For example, biomarkers for usein distinguishing, or aiding in distinguishing, atherosclerotic subjectsfrom non-atherosclerotic subjects include one or more of thosebiomarkers listed in Tables 14, 15, 16, 17, 3-methylhistidine, p-cresolsulfate, mannose, glucose, and/or gluconate, and combinations thereof.In one aspect biomarkers for use in methods relating to atherosclerosisusing plasma samples from a subject include one or more of3-methylhistidine, p-cresol sulfate, mannose, glucose, gluconate, andthose listed in Tables 14 and 17. In another aspect biomarkers for usein methods relating to atherosclerosis using aortic samples from asubject include one or more of those listed in Table 15. In yet anotheraspect, biomarkers for use in methods relating to atherosclerosis usingliver samples from a subject include one or more of those listed inTable 16. In one aspect, preferred biomarkers for use in methodsinvolving subjects in an early stage of atherosclerosis include thebiomarkers identified in FIGS. 10A, 11A, and 12A. Preferred biomarkersfor use in methods involving subjects in a mid-stage of atherosclerosisinclude the biomarkers identified in FIGS. 10B, 11B, and 12B. Preferredbiomarkers for use in methods involving subjects in a later stage ofatherosclerosis include the biomarkers identified in FIGS. 10C, 11C, and12C. Preferred biomarkers for use in methods involving subjects in anystage of atherosclerosis include the biomarkers identified in FIGS. 10D,11D, and 12D.

Biomarkers for use in methods disclosed herein relating tocardiomyopathy include one or more of those biomarkers listed in Tables21, 22, 23, and/or 25. Such markers may be used, for example, todistinguish, or aiding in distinguishing, between subjects havingcardiomyopathy from subjects not having cardiomyopathy. In one aspect,biomarkers for use in methods relating to cardiomyopathy using cardiactissue samples from a subject include one or more of those listed inTable 21. In another aspect, biomarkers for use in methods relating tocardiomyopathy using plasma samples from a subject include one or moreof those listed in Table 22 and/or 23.

Biomarkers for use in methods disclosed herein relating to obesityinclude one or more of those biomarkers listed in Table 26. Such markersmay be used, for example, to distinguish obese subjects from leansubjects. Such markers may also be used in combination with biomarkersfor pre-diabetes, metabolic syndrome, atherosclerosis, orcardiomyopathy. In another aspect, the markers may be used, for example,to determine susceptibility to obesity or weight gain. In anotheraspect, the markers may be used, for example, to determine if atherapeutic agent is likely to induce weight gain in a subject.

Any number of biomarkers may be used in the methods disclosed herein.That is, the disclosed methods may include the determination of thelevel(s) of one biomarker, two or more biomarkers, three or morebiomarkers, four or more biomarkers, five or more biomarkers, six ormore biomarkers, seven or more biomarkers, eight or more biomarkers,nine or more biomarkers, ten or more biomarkers, fifteen or morebiomarkers, etc., including a combination of all of the biomarkers ineach or all of Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23,25, 26, 27, 28, 29, or any fraction thereof. In another aspect, thenumber of biomarkers for use in the disclosed methods include the levelsof about thirty or less biomarkers, twenty-five or less, twenty or less,fifteen or less, ten or less, nine or less, eight or less, seven orless, six or less, five or less biomarkers. In another aspect, thenumber of biomarkers for use in the disclosed methods include the levelsof one, two, three, four, five, six, seven, eight, nine, ten, eleven,twelve, thirteen, fourteen, fifteen, twenty, twenty-five, or thirtybiomarkers.

Although the identities of some of the biomarkers compounds are notknown at this time, such identities are not necessary for theidentification of the biomarkers in biological samples from subjects, asthe “unnamed” compounds have been sufficiently characterized byanalytical techniques to allow such identification. The analyticalcharacterization of all such “unnamed” compounds is listed in theExamples. Such “unnamed” biomarkers are designated herein using thenomenclature “Metabolite” followed by a specific metabolite number.

In addition, the methods disclosed herein using the biomarkers listed inthe tables may be used in combination with clinical diagnostic measuresof the respective conditions. Combinations with clinical diagnostics mayfacilitate the disclosed methods, or confirm results of the disclosedmethods (for example, facilitating or confirming diagnosis, monitoringprogression or regression, and/or determining predisposition topre-diabetes).

Finally, where the potential identity of a compound is proposed for an“unnamed” metabolite and such identity has not been confirmed, thenomenclature of “possible” (along with the potential compound identity)follows the “Metabolite” number. Such proposed identity should not beconsidered as limiting the analytical characterization of the otherwise“unnamed” compounds.

II. Diagnostic Methods

The biomarkers described herein may be used to diagnose, or to aid indiagnosing, whether a subject has a disease or condition, such asinsulin resistance, pre-diabetes, type-2 diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy. For example, biomarkers for use indiagnosing, or aiding in diagnosing, whether a subject has a insulinresistance include one or more of those identified in Tables 4, 5, 6, 7,8, 9A, 9B, 27, 28, 29, and combinations thereof. In one embodiment, thebiomarkers include one or more of those identified in Tables 4, 5, 6, 7,8, 9A, 9B, 27, 28, and combinations thereof. In another embodiment,combinations of biomarkers include those, such as 2-hydroxybutyrate incombination with one or more biomarkers indentified in Tables 4, 5, 6,7, 8, 9A, 9B, 27, 28, and/or 29.

Methods for diagnosing, or aiding in diagnosing, whether a subject has adisease or condition, such as pre-diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy, may performed using one or more ofthe biomarkers identified in the respective tables provided herein. Amethod of diagnosing (or aiding in diagnosing) whether a subject has adisease or condition, such as pre-diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy, comprises (1) analyzing a biologicalsample from a subject to determine the level(s) of one or morebiomarkers of the disease or condition in the sample and (2) comparingthe level(s) of one or more biomarkers in the sample to disease- orcondition-positive and/or disease- or condition-negative referencelevels of the one or more biomarkers to diagnose (or aid in thediagnosis of) whether the subject has the disease or condition. Forexample, a method of diagnosing (or aiding in diagnosing) whether asubject is pre-diabetic comprises (1) analyzing a biological sample froma subject to determine the level(s) of one or more biomarkers ofpre-diabetes in the sample and (2) comparing the level(s) of the one ormore biomarkers in the sample to pre-diabetes-positive and/orpre-diabetes-negative reference levels of the one or more biomarkers inorder to diagnose (or aid in the diagnosis of) whether the subject haspre-diabetes. The one or more biomarkers that are used are selected fromTables 4, 5, 6, 7, 8, 9A, 9B, and combinations thereof. When such amethod is used in aiding in the diagnosis of a disease or condition,such as insulin resistance, pre-diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy, the results of the method may beused along with other methods (or the results thereof) useful in theclinical determination of whether a subject has a given disease orcondition. Methods useful in the clinical determination of whether asubject has a disease or condition such as pre-diabetes, metabolicsyndrome, atherosclerosis, or cardiomyopathy are known in the art. Forexample, methods useful in the clinical determination of whether asubject has pre-diabetes include, for example, glucose disposal rates(Rd), body weight measurements, waist circumference measurements, BMIdeterminations, Peptide YY measurements, Hemoglobin A1C measurements,adiponectin measurements, fasting plasma glucose measurements, freefatty acid measurements, fasting plasma insulin measurements, and thelike. Methods useful for the clinical determination of atherosclerosisand/or cardiomyopathy in a subject include angiography, stress-testing,blood tests (e.g. to measure homocysteine, fibrinogen, lipoprotein (a),small LDL particles, and c-reactive protein levels),electrocardiography, echocardiography, computed tomography (CT) scans,ankle/brachial index, and intravascular ultrasounds.

In another example, the identification of biomarkers for diseases orconditions such as insulin resistance, pre-diabetes, metabolic syndrome,atherosclerosis, and cardiomyopathy, allows for the diagnosis of (or foraiding in the diagnosis of) such diseases or conditions in subjectspresenting one or more symptoms of the disease or condition. Forexample, a method of diagnosing (or aiding in diagnosing) whether asubject has insulin resistance comprises (1) analyzing a biologicalsample from a subject to determine the level(s) of one or morebiomarkers of insulin resistance in the sample and (2) comparing thelevel(s) of the one or more biomarkers in the sample to insulinresistance-positive and/or insulin resistance-negative reference levelsof the one or more biomarkers in order to diagnose (or aid in thediagnosis of) whether the subject has insulin resistance. The one ormore biomarkers that are used are selected from Tables 4, 5, 6, 7, 8,9A, and/or 9B. The biomarkers for insulin resistance may also be used toclassify subjects as being either insulin resistant, insulin sensitive,or having impaired insulin sensitivity. As described in Example 2,below, biomarkers are identified that may be used to classify subjectsas being insulin resistant, insulin sensitive, or having impairedinsulin sensitivity. The biomarkers in Tables 4, 5, 6, 7, 8, 9A, and/or9B, may also be used to classify subjects as having impaired fastingglucose levels or impaired glucose tolerance or normal glucosetolerance. Thus, the biomarkers may indicate compounds that increase anddecrease as the glucose disposal rate increases. By determiningappropriate reference levels of the biomarkers for each group (insulinresistant, insulin impaired, insulin sensitive), subjects can bediagnosed appropriately. The results of this method may be combined withthe results of clinical measurements to aid in the diagnosis of insulinresistance or for categorizing the subject as having NGT, IFG, or IGT.

Increased insulin resistance correlates with the glucose disposal rate(Rd) as measured by the HI clamp. As exemplified below, metabolomicanalysis was carried out to identify biomarkers that correlate with theglucose disposal rate (Rd). These biomarkers can be used in amathematical model to determine the glucose disposal rate of thesubject. The insulin sensitivity of the individual can be determinedusing this model. Using metabolomic analysis, panels of metabolites thatcan be used in a simple blood test to predict insulin resistance asmeasured by the “gold standard” of hyperinsulinemic euglycemic clamps inat least two independent cohorts of subjects were discovered. In anotherexample, biomarkers are identified that correlate with the results oforal glucose tolerance tests (OGTT) for use in categorizing subjects ashaving normal glucose tolerance (NGT), impaired fasting glucose levels(IFG), or impaired glucose tolerance (IGT).

Independent studies were carried out to identify a set of biomarkersthat when used with a polynomic algorithm will enable the earlydetection of changes in insulin resistance in a subject. In one aspect,the instant invention provides the subject with a score indicating thelevel of insulin resistance (“IR Score”) of the subject. The score isbased upon clinically significant changed reference level for abiomarker and/or combination of biomarkers. The reference level can bederived from an algorithm or computed from indices for impaired glucosetolerance and can be presented in a report as shown in FIG. 5. The IRScore places the subject in the range of insulin resistance from normal(i.e. insulin sensitive) to high. Disease progression or remission canbe monitored by periodic determination and monitoring of the IR Score.Response to therapeutic intervention can be determined by monitoring theIR Score. The IR Score can also be used to evaluate drug efficacy.

Methods for determining a subject's insulin resistance score (IR score)may be performed using one or more of the biomarkers identified in therespective Tables provided herein. For example, a method for determiningthe IR score of a subject comprises the steps of: (1) analyzing abiological sample from a subject to determine the level(s) of one ormore insulin resistance biomarkers in the sample, and (2) comparing thelevel(s) of the one or more insulin resistance biomarkers in the sampleto insulin resistance reference levels of the one or more biomarkers inorder to determine the subject's insulin resistance score. The one ormore biomarkers that are used may be selected from Tables 4, 5, 6, 7, 8,9A, 9B, and combinations thereof. The method may employ any number ofmarkers selected from Tables 4, 5, 6, 7, 8, 9A, and/or 9B, including 1,2, 3, 4, 5, 6, 7, 8, 9, 10, or more markers. Multiple biomarkers may becorrelated with a given condition, such as insulin resistance, by anymethod, including statistical methods such as regression analysis.

Also as exemplified below, metabolomic analysis was carried out toidentify biomarkers that correlate with metabolic syndrome,atherosclerosis, cardiomyopathy, and other diseases or conditions. Suchbiomarkers may be used in the methods of the present invention toanalyze biological samples to identify or measure the level of thebiomarkers in the sample.

Any suitable method may be used to analyze the biological sample inorder to determine the level(s) of the one or more biomarkers in thesample. Suitable methods include chromatography (e.g., HPLC, gaschromatography, liquid chromatography), mass spectrometry (e.g., MS,MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage,other immunochemical techniques, and combinations thereof. Further, thelevel(s) of the one or more biomarkers may be measured indirectly, forexample, by using an assay that measures the level of a compound (orcompounds) that correlates with the level of the biomarker(s) that aredesired to be measured.

After the level(s) of the one or more biomarker(s) is determined, thelevel(s) may be compared to disease or condition reference level(s) ofthe one or more biomarker(s) to determine a rating for each of the oneor more biomarker(s) in the sample. The rating(s) may be aggregatedusing any algorithm to create a score, for example, an insulinresistance (IR) score, for the subject. The algorithm may take intoaccount any factors relating to the disease or condition, such asinsulin resistance, including the number of biomarkers, the correlationof the biomarkers to the disease or condition, etc.

In one example, the subject's insulin resistance score may be correlatedto any index indicative of a level insulin resistance, from normalglucose tolerance to insulin resistant. For example, a subject having aninsulin resistance score of less than 25 may indicate that the subjecthas normal glucose tolerance; a score of between 26 and 50 may indicatethat the subject has low impaired glucose tolerance; a score of between51 and 75 may indicate that the subject has medium impaired glucosetolerance; a score of between 76 and 100 may indicate that the subjecthas high impaired glucose tolerance; and a score above 100 may indicatethat the subject has type-2 diabetes.

III. Monitoring Disease or Condition Progression/Regression

The identification of biomarkers herein allows for monitoringprogression/regression of the respective diseases or conditions (e.g.pre-diabetes, metabolic syndrome, atherosclerosis, cardiomyopathy,insulin resistance, etc.) in a subject. A method of monitoring theprogression/regression of disease or condition, such as pre-diabetes,type-2 diabetes, metabolic syndrome, atherosclerosis, andcardiomyopathy, in a subject comprises (1) analyzing a first biologicalsample from a subject to determine the level(s) of one or morebiomarkers for the respective disease or condition selected from Tables4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, 28, andcombinations thereof in the first sample obtained from the subject at afirst time point, (2) analyzing a second biological sample from asubject to determine the level(s) of the one or more biomarkers, thesecond sample obtained from the subject at a second time point, and (3)comparing the level(s) of one or more biomarkers in the first sample tothe level(s) of the one or more biomarkers in the second sample in orderto monitor the progression/regression of the disease or condition in thesubject. The results of the method are indicative of the course of thedisease or condition (i.e., progression or regression, if any change) inthe subject.

In one embodiment, the results of the method may be based on InsulinResistance (IR) Score which is indicative of the insulin resistance inthe subject and which can be monitored over time. By comparing the IRScore from a first time point sample to the IR Score from at least asecond time point sample the progression or regression of IR can bedetermined. Such a method of monitoring the progression/regression ofpre-diabetes and/or type-2 diabetes in a subject comprises (1) analyzinga first biological sample from a subject to determine an IR score forthe first sample obtained from the subject at a first time point, (2)analyzing a second biological sample from a subject to determine asecond IR score, the second sample obtained from the subject at a secondtime point, and (3) comparing the IR score in the first sample to the IRscore in the second sample in order to monitor theprogression/regression of pre-diabetes and/or type-2 diabetes in thesubject.

Using the biomarkers and algorithm of the instant invention forprogression monitoring may guide, or assist a physician's decision toimplement preventative measures such as dietary restrictions, exercise,or early-stage drug treatment.

IV. Determining Predisposition to a Disease or Condition

The biomarkers identified herein may also be used in the determinationof whether a subject not exhibiting any symptoms of a disease orcondition, such as pre-diabetes, type-2 diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy. The biomarkers may be used, forexample, to determine whether a subject is predisposed to developing,for example, insulin resistance. Such methods of determining whether asubject having no symptoms of a particular disease or condition such aspre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, orcardiomyopathy, is predisposed to developing a particular disease orcondition comprise (1) analyzing a biological sample from a subject todetermine the level(s) of one or more biomarkers listed in therespective tables (e.g. Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17,21, 22, 23, 25, 26, and combinations thereof) in the sample and (2)comparing the level(s) of the one or more biomarkers in the sample todisease- or condition-positive and/or disease- or condition-negativereference levels of the one or more biomarkers in order to determinewhether the subject is predisposed to developing the respective diseaseor condition. For example, the identification of biomarkers for insulinresistance allows for the determination of whether a subject having nosymptoms of insulin resistance is predisposed to developing insulinresistance. A method of determining whether a subject having no symptomsof insulin resistance is predisposed to developing insulin resistancecomprises (1) analyzing a biological sample from a subject to determinethe level(s) of one or more biomarkers listed in Tables 4, 5, 6, 7, 8,9A, and 9B, and combinations thereof in the sample and (2) comparing thelevel(s) of the one or more biomarkers in the sample to insulinresistance-positive and/or insulin resistance-negative reference levelsof the one or more biomarkers in order to determine whether the subjectis predisposed to developing insulin resistance. The results of themethod may be used along with other methods (or the results thereof)useful in the clinical determination of whether a subject is predisposedto developing the disease or condition.

After the level(s) of the one or more biomarkers in the sample aredetermined, the level(s) are compared to disease- or condition-positiveand/or disease- or condition-negative reference levels in order topredict whether the subject is predisposed to developing a disease orcondition such as pre-diabetes, type-2 diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy. Levels of the one or more biomarkersin a sample corresponding to the disease- or condition-positivereference levels (e.g., levels that are the same as the referencelevels, substantially the same as the reference levels, above and/orbelow the minimum and/or maximum of the reference levels, and/or withinthe range of the reference levels) are indicative of the subject beingpredisposed to developing the disease or condition. Levels of the one ormore biomarkers in a sample corresponding to disease- orcondition-negative reference levels (e.g., levels that are the same asthe reference levels, substantially the same as the reference levels,above and/or below the minimum and/or maximum of the reference levels,and/or within the range of the reference levels) are indicative of thesubject not being predisposed to developing the disease or condition. Inaddition, levels of the one or more biomarkers that are differentiallypresent (especially at a level that is statistically significant) in thesample as compared to disease- or condition-negative reference levelsmay be indicative of the subject being predisposed to developing thedisease or condition. Levels of the one or more biomarkers that aredifferentially present (especially at a level that is statisticallysignificant) in the sample as compared to disease-condition-positivereference levels are indicative of the subject not being predisposed todeveloping the disease or condition.

By way of example, after the level(s) of the one or more biomarkers inthe sample are determined, the level(s) are compared to insulinresistance-positive and/or insulin resistance-negative reference levelsin order to predict whether the subject is predisposed to developinginsulin resistance. Levels of the one or more biomarkers in a samplecorresponding to the insulin resistance-positive reference levels (e.g.,levels that are the same as the reference levels, substantially the sameas the reference levels, above and/or below the minimum and/or maximumof the reference levels, and/or within the range of the referencelevels) are indicative of the subject being predisposed to developinginsulin resistance. Levels of the one or more biomarkers in a samplecorresponding to the insulin resistance-negative reference levels (e.g.,levels that are the same as the reference levels, substantially the sameas the reference levels, above and/or below the minimum and/or maximumof the reference levels, and/or within the range of the referencelevels) are indicative of the subject not being predisposed todeveloping insulin resistance. In addition, levels of the one or morebiomarkers that are differentially present (especially at a level thatis statistically significant) in the sample as compared to insulinresistance-negative reference levels are indicative of the subject beingpredisposed to developing insulin resistance. Levels of the one or morebiomarkers that are differentially present (especially at a level thatis statistically significant) in the sample as compared to insulinresistance-positive reference levels are indicative of the subject notbeing predisposed to developing insulin resistance. Although insulinresistance is discussed in this example, predisposition to the otherdiseases or conditions may also be determined in accordance with thismethod by using one or more of the respective biomarkers as set forthabove.

Furthermore, it may also be possible to determine reference levelsspecific to assessing whether or not a subject that does not have adisease or condition such as insulin resistance, pre-diabetes, type-2diabetes, metabolic syndrome, atherosclerosis, or cardiomyopathy, ispredisposed to developing a disease or condition. For example, it may bepossible to determine reference levels of the biomarkers for assessingdifferent degrees of risk (e.g., low, medium, high) in a subject fordeveloping a disease or condition. Such reference levels could be usedfor comparison to the levels of the one or more biomarkers in abiological sample from a subject.

V. Monitoring Therapeutic Efficacy:

The biomarkers provided also allow for the assessment of the efficacy ofa composition for treating a disease or condition such as insulinresistance, pre-diabetes, type-2 diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy. For example, the identification ofbiomarkers for insulin resistance also allows for assessment of theefficacy of a composition for treating insulin resistance as well as theassessment of the relative efficacy of two or more compositions fortreating insulin resistance. Such assessments may be used, for example,in efficacy studies as well as in lead selection of compositions fortreating the disease or condition.

Thus, also provided are methods of assessing the efficacy of acomposition for treating a disease or condition such as insulinresistance, pre-diabetes, type-2 diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy comprising (1) analyzing, from asubject (or group of subjects) having a disease or condition such aspre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, orcardiomyopathy and currently or previously being treated with acomposition, a biological sample (or group of samples) to determine thelevel(s) of one or more biomarkers for the disorder selected from Tables4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, 28, andcombinations thereof, and (2) comparing the level(s) of the one or morebiomarkers in the sample to (a) level(s) of the one or more biomarkersin a previously-taken biological sample from the subject, wherein thepreviously-taken biological sample was obtained from the subject beforebeing treated with the composition, (b) disease- or condition-positivereference levels of the one or more biomarkers, (c) disease- orcondition-negative reference levels of the one or more biomarkers, (d)disease- or condition-progression-positive reference levels of the oneor more biomarkers, and/or (e) disease- or condition-regression-positivereference levels of the one or more biomarkers. The results of thecomparison are indicative of the efficacy of the composition fortreating the respective disease or condition.

The change (if any) in the level(s) of the one or more biomarkers overtime may be indicative of progression or regression of the disease orcondition in the subject. To characterize the course of a given diseaseor condition in the subject, the level(s) of the one or more biomarkersin the first sample, the level(s) of the one or more biomarkers in thesecond sample, and/or the results of the comparison of the levels of thebiomarkers in the first and second samples may be compared to therespective disease- or condition-positive and/or disease- orcondition-negative reference levels of the one or more biomarkers. Ifthe comparisons indicate that the level(s) of the one or more biomarkersare increasing or decreasing over time (e.g., in the second sample ascompared to the first sample) to become more similar to the disease- orcondition-positive reference levels (or less similar to the disease- orcondition-negative reference levels), then the results are indicative ofthe disease's or condition's progression. If the comparisons indicatethat the level(s) of the one or more biomarkers are increasing ordecreasing over time to become more similar to the disease- orcondition-negative reference levels (or less similar to the disease- orcondition-positive reference levels), then the results are indicative ofthe disease's or condition's regression.

For example, in order to characterize the course of insulin resistancein the subject, the level(s) of the one or more biomarkers in the firstsample, the level(s) of the one or more biomarkers in the second sample,and/or the results of the comparison of the levels of the biomarkers inthe first and second samples may be compared to insulinresistance-positive and/or insulin resistance-negative reference levelsof the one or more biomarkers. If the comparisons indicate that thelevel(s) of the one or more biomarkers are increasing or decreasing overtime (e.g., in the second sample as compared to the first sample) tobecome more similar to the insulin resistance-positive reference levels(or less similar to the insulin resistance-negative reference levels),then the results are indicative of insulin resistance progression. Ifthe comparisons indicate that the level(s) of the one or more biomarkersare increasing or decreasing over time to become more similar to theinsulin resistance-negative reference levels (or less similar to theinsulin resistance-positive reference levels), then the results areindicative of insulin resistance regression.

The second sample may be obtained from the subject any period of timeafter the first sample is obtained. In one aspect, the second sample isobtained 1, 2, 3, 4, 5, 6, or more days after the first sample or afterthe initiation of the administration of a composition. In anotheraspect, the second sample is obtained 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ormore weeks after the first sample or after the initiation of theadministration of a composition. In another aspect, the second samplemay be obtained 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or more monthsafter the first sample or after the initiation of the administration ofa composition.

The course of a disease or condition such as insulin resistance,pre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, orcardiomyopathy in a subject may also be characterized by comparing thelevel(s) of the one or more biomarkers in the first sample, the level(s)of the one or more biomarkers in the second sample, and/or the resultsof the comparison of the levels of the biomarkers in the first andsecond samples to disease- or condition-progression-positive and/ordisease- or condition-regression-positive reference levels. If thecomparisons indicate that the level(s) of the one or more biomarkers areincreasing or decreasing over time (e.g., in the second sample ascompared to the first sample) to become more similar to the disease- orcondition-progression-positive reference levels (or less similar to thedisease- or condition-regression-positive reference levels), then theresults are indicative of the disease or condition progression. If thecomparisons indicate that the level(s) of the one or more biomarkers areincreasing or decreasing over time to become more similar to thedisease- or condition-regression-positive reference levels (or lesssimilar to the disease- or condition-progression-positive referencelevels), then the results are indicative of disease or conditionregression.

As with the other methods described herein, the comparisons made in themethods of monitoring progression/regression of a disease or conditionsuch as insulin resistance, pre-diabetes, type-2 diabetes, metabolicsyndrome, atherosclerosis, or cardiomyopathy in a subject may be carriedout using various techniques, including simple comparisons, one or morestatistical analyses, and combinations thereof.

The results of the method may be used along with other methods (or theresults thereof) useful in the clinical monitoring ofprogression/regression of the disease or condition in a subject.

As described above in connection with methods of diagnosing (or aidingin the diagnosis of) a disease or condition such as insulin resistance,pre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, orcardiomyopathy, any suitable method may be used to analyze thebiological samples in order to determine the level(s) of the one or morebiomarkers in the samples. In addition, the level(s) one or morebiomarkers, including a combination of all of the biomarkers in Tables4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, and/or28, or any fraction thereof, may be determined and used in methods ofmonitoring progression/regression of the respective disease or conditionin a subject.

Such methods could be conducted to monitor the course of disease orcondition development in subjects, for example the course ofpre-diabetes to type-2 diabetes in a subject having pre-diabetes, orcould be used in subjects not having a disease or condition (e.g.,subjects suspected of being predisposed to developing the disease orcondition) in order to monitor levels of predisposition to the diseaseor condition.

Clinical studies from around the world have been carried out to testwhether anti-diabetic therapies, such as metformin or acarbose, canprevent diabetes progression in pre-diabetic patients. These studieshave shown that such therapies can prevent diabetes onset. From the U.S.Diabetes Prevention Program (DPP), metformin reduced the rate ofprogression to diabetes by 38% and lifestyle and exercise interventionreduced the rate of progression to diabetes by 56%. Because of suchsuccesses, the ADA has revised its 2008 Standards of Medical Care inDiabetes to include the following statements in the section onPrevention/Delay of Type 2 Diabetes: “In addition to lifestylecounseling, metformin may be considered in those who are at very highrisk (combined IFG and IGT plus other risk factors) and who are obeseand under 60 years of age.”

Pharmaceutical companies have carried out studies to assess whethercertain classes of drugs, such as the PPARy class of insulinsensitizers, can prevent diabetes progression. Similar to the DPP trial,some of these studies have shown great promise and success forpreventing diabetes, whereas others have exposed a certain amount ofrisk associated with certain anti-diabetic pharmacologic treatments whengiven to the general pre-diabetic population as defined by current IRdiagnostics. Pharmaceutical companies are in need of diagnostics thatcan identify and stratify high risk pre-diabetics so they can assess theefficacy of their pre-diabetic therapeutic candidates more effectivelyand safely.

Considering the infrequency of the oral glucose tolerance test (OGTT)procedures in the clinical setting, a new diagnostic test that directlymeasures insulin resistance in a fasted sample would enable a physicianto identify and stratify patients who are moving toward the etiology ofpre-diabetes and cardiovascular disease much earlier.

VI. Identification of Responders and Non-responders to Therapeutic:

The biomarkers provided also allow for the identification of subjects inwhom the composition for treating a disease or condition such aspre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, orcardiomyopathy is efficacious (i.e. patient responds to therapeutic).For example, the identification of biomarkers for insulin resistancealso allows for assessment of the subject response to a composition fortreating insulin resistance as well as the assessment of the relativepatient response to two or more compositions for treating insulinresistance. Such assessments may be used, for example, in selection ofcompositions for treating the disease or condition for certain subjects.

Thus, also provided are methods of predicting the response of a patientto a composition for treating a disease or condition such aspre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, orcardiomyopathy comprising (1) analyzing, from a subject (or group ofsubjects) having a disease or condition such as pre-diabetes, type-2diabetes, metabolic syndrome, atherosclerosis, or cardiomyopathy andcurrently or previously being treated with a composition, a biologicalsample (or group of samples) to determine the level(s) of one or morebiomarkers for the disorder selected from Tables 4, 5, 6, 7, 8, 9A, 9B,14, 15, 16, 17, 21, 22, 23, 25, 26, 27, and 28, and combinationsthereof, and (2) comparing the level(s) of the one or more biomarkers inthe sample to (a) level(s) of the one or more biomarkers in apreviously-taken biological sample from the subject, wherein thepreviously-taken biological sample was obtained from the subject beforebeing treated with the composition, (b) disease- or condition-positivereference levels of the one or more biomarkers, (c) disease- orcondition-negative reference levels of the one or more biomarkers, (d)disease- or condition-progression-positive reference levels of the oneor more biomarkers, and/or (e) disease- or condition-regression-positivereference levels of the one or more biomarkers. The results of thecomparison are indicative of the response of the patient to thecomposition for treating the respective disease or condition.

The change (if any) in the level(s) of the one or more biomarkers overtime may be indicative of response of the subject to the therapeutic. Tocharacterize the course of a given therapeutic in the subject, thelevel(s) of the one or more biomarkers in the first sample, the level(s)of the one or more biomarkers in the second sample, and/or the resultsof the comparison of the levels of the biomarkers in the first andsecond samples may be compared to the respective disease- orcondition-positive and/or disease- or condition-negative referencelevels of the one or more biomarkers. If the comparisons indicate thatthe level(s) of the one or more biomarkers are increasing or decreasingover time (e.g., in the second sample as compared to the first sample)to become more similar to the disease- or condition-positive referencelevels (or less similar to the disease- or condition-negative referencelevels), then the results are indicative of the patient not respondingto the therapeutic. If the comparisons indicate that the level(s) of theone or more biomarkers are increasing or decreasing over time to becomemore similar to the disease- or condition-negative reference levels (orless similar to the disease- or condition-positive reference levels),then the results are indicative of the patient responding to thetherapeutic.

For example, in order to characterize the patient response to atherapeutic for insulin resistance, the level(s) of the one or morebiomarkers in the first sample, the level(s) of the one or morebiomarkers in the second sample, and/or the results of the comparison ofthe levels of the biomarkers in the first and second samples may becompared to insulin resistance-positive and/or insulinresistance-negative reference levels of the one or more biomarkers. Ifthe comparisons indicate that the level(s) of the one or more biomarkersare increasing or decreasing over time (e.g., in the second sample ascompared to the first sample) to become more similar to the insulinresistance-positive reference levels (or less similar to the insulinresistance-negative reference levels), then the results are indicativeof non-response to the therapeutic. If the comparisons indicate that thelevel(s) of the one or more biomarkers are increasing or decreasing overtime to become more similar to the insulin resistance-negative referencelevels (or less similar to the insulin resistance-positive referencelevels), then the results are indicative of response to the therapeutic.

The second sample may be obtained from the subject any period of timeafter the first sample is obtained. In one aspect, the second sample isobtained 1, 2, 3, 4, 5, 6, or more days after the first sample. Inanother aspect, the second sample is obtained 1, 2, 3, 4, 5, 6, 7, 8, 9,10, or more weeks after the first sample or after the initiation oftreatment with the composition. In another aspect, the second sample maybe obtained 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or more months afterthe first sample or after the initiation of treatment with thecomposition.

As with the other methods described herein, the comparisons made in themethods of determining a patient response to a therapeutic for a diseaseor condition such as insulin resistance, pre-diabetes, type-2 diabetes,metabolic syndrome, atherosclerosis, or cardiomyopathy in a subject maybe carried out using various techniques, including simple comparisons,one or more statistical analyses, and combinations thereof.

The results of the method may be used along with other methods (or theresults thereof) useful in determining a patient response to atherapeutic for the disease or condition in a subject.

As described above in connection with methods of diagnosing (or aidingin the diagnosis of) a disease or condition such as pre-diabetes, type-2diabetes, metabolic syndrome, atherosclerosis, or cardiomyopathy, anysuitable method may be used to analyze the biological samples in orderto determine the level(s) of the one or more biomarkers in the samples.In addition, the level(s) one or more biomarkers, including acombination of all of the biomarkers in Tables 4, 5, 6, 7, 8, 9A, 9B,14, 15, 16, 17, 21, 22, 23, 25, 26, 27, and/or 28, or any fractionthereof, may be determined and used in methods of monitoringprogression/regression of the respective disease or condition in asubject.

Such methods could be conducted to monitor the patient response to atherapeutic for a disease or condition development in subjects, forexample the course of pre-diabetes to type-2 diabetes in a subjecthaving pre-diabetes, or could be used in subjects not having a diseaseor condition (e.g., subjects suspected of being predisposed todeveloping the disease or condition) in order to monitor levels ofpredisposition to the disease or condition.

Pharmaceutical companies have carried out studies to assess whethercertain classes of drugs, such as the PPARy class of insulinsensitizers, can prevent diabetes progression. Some of these studieshave shown great promise and success for preventing diabetes, whereasothers have exposed a certain amount of risk associated with certainanti-diabetic pharmacologic treatments when given to the generalpre-diabetic population as defined by current IR diagnostics.Pharmaceutical companies are in need of diagnostics that can identifyresponders and non-responders in order to stratify high riskpre-diabetics to assess the efficacy of their pre-diabetic therapeuticcandidates more effectively and safely. A new diagnostic test thatdiscriminates non-responding from responding patients to a therapeuticwould enable pharmaceutical companies to identify and stratify patientsthat are likely to respond to the therapeutic agent and target specifictherapeutics for certain cohorts that are likely to respond to thetherapeutic.

VII. Methods of Screening a Composition for Activity in ModulatingBiomarkers

The biomarkers provided herein also allow for the screening ofcompositions for activity in modulating biomarkers associated with adisease or condition, such as pre-diabetes, type-2 diabetes, metabolicsyndrome, atherosclerosis, and cardiomyopathy, which may be useful intreating the disease or condition. Such methods comprise assaying testcompounds for activity in modulating the levels of one or morebiomarkers selected from the respective biomarkers listed in therespective tables. Such screening assays may be conducted in vitroand/or in vivo, and may be in any form known in the art useful forassaying modulation of such biomarkers in the presence of a testcomposition such as, for example, cell culture assays, organ cultureassays, and in vivo assays (e.g., assays involving animal models). Forexample, the identification of biomarkers for insulin resistance alsoallows for the screening of compositions for activity in modulatingbiomarkers associated with insulin resistance, which may be useful intreating insulin resistance. Methods of screening compositions usefulfor treatment of insulin resistance comprise assaying test compositionsfor activity in modulating the levels of one or more biomarkers inTables 4, 5, 6, 7, 8, 9A, 9B, 27, and/or 28. Although insulin resistanceis discussed in this example, the other diseases and conditions such aspre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, andcardiomyopathy may also be diagnosed or aided to be diagnosed inaccordance with this method by using one or more of the respectivebiomarkers as set forth above.

The methods for screening a composition for activity in modulating oneor more biomarkers of a disease or condition such as insulin resistance,pre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, andcardiomyopathy comprise (1) contacting one or more cells with acomposition, (2) analyzing at least a portion of the one or more cellsor a biological sample associated with the cells to determine thelevel(s) of one or more biomarkers of a disease or condition selectedfrom the biomarkers provided in Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15,16, 17, 21, 22, 23, 25, and/or 26; and (3) comparing the level(s) of theone or more biomarkers with predetermined standard levels for the one ormore biomarkers to determine whether the composition modulated thelevel(s) of the one or more biomarkers. In one embodiment, a method forscreening a composition for activity in modulating one or morebiomarkers of insulin resistance comprises (1) contacting one or morecells with a composition, (2) analyzing at least a portion of the one ormore cells or a biological sample associated with the cells to determinethe level(s) of one or more biomarkers of insulin resistance selectedfrom Tables 4, 5, 6, 7, 8, 9A, and/or 9B; and (3) comparing the level(s)of the one or more biomarkers with predetermined standard levels for theone or more biomarkers to determine whether the composition modulatedthe level(s) of the one or more biomarkers. As discussed above, thecells may be contacted with the composition in vitro and/or in vivo. Thepredetermined standard levels for the one or more biomarkers may be thelevels of the one or more biomarkers in the one or more cells in theabsence of the composition. The predetermined standard levels for theone or more biomarkers may also be the level(s) of the one or morebiomarkers in control cells not contacted with the composition.

In addition, the methods may further comprise analyzing at least aportion of the one or more cells or a biological sample associated withthe cells to determine the level(s) of one or more non-biomarkercompounds of a disease or condition, such as pre-diabetes, type-2diabetes, metabolic syndrome, atherosclerosis, and cardiomyopathy. Thelevels of the non-biomarker compounds may then be compared topredetermined standard levels of the one or more non-biomarkercompounds.

Any suitable method may be used to analyze at least a portion of the oneor more cells or a biological sample associated with the cells in orderto determine the level(s) of the one or more biomarkers (or levels ofnon-biomarker compounds). Suitable methods include chromatography (e.g.,HPLC, gas chromatography, liquid chromatography), mass spectrometry(e.g., MS, MS-MS), ELISA, antibody linkage, other immunochemicaltechniques, biochemical or enzymatic reactions or assays, andcombinations thereof. Further, the level(s) of the one or morebiomarkers (or levels of non-biomarker compounds) may be measuredindirectly, for example, by using an assay that measures the level of acompound (or compounds) that correlates with the level of thebiomarker(s) (or non-biomarker compounds) that are desired to bemeasured.

VIII. Method of Identifying Potential Drug Targets

The disclosure also provides methods of identifying potential drugtargets for diseases or conditions such as insulin resistance,pre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, andcardiomyopathy, using the biomarkers listed in Tables 4, 5, 6, 7, 8, 9A,9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, and/or 28. A method foridentifying a potential drug target for a disease or condition such aspre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis andcardiomyopathy comprises (1) identifying one or more biochemicalpathways associated with one or more biomarkers for a metabolicsyndrome-related metabolic disorder selected from the respective tables(Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27,and/or 28); and (2) identifying a protein (e.g., an enzyme) affecting atleast one of the one or more identified biochemical pathways, theprotein being a potential drug target for the disease or condition. Forexample, the identification of biomarkers for insulin resistance alsoallows for the identification of potential drug targets for insulinresistance. A method for identifying a potential drug target for insulinresistance comprises (1) identifying one or more biochemical pathwaysassociated with one or more biomarkers for insulin resistance selectedfrom Tables 4, 5, 6, 7, 8, 9A, 9B, 27, and/or 28, and (2) identifying aprotein (e.g., an enzyme) affecting at least one of the one or moreidentified biochemical pathways, the protein being a potential drugtarget for insulin resistance. Although insulin resistance is discussedin this example, the other diseases or conditions such as type-2diabetes, metabolic syndrome, atherosclerosis and cardiomyopathy, mayalso be diagnosed or aided to be diagnosed in accordance with thismethod by using one or more of the respective biomarkers as set forthabove.

Another method for identifying a potential drug target for a disease orcondition such as pre-diabetes, type-2 diabetes, metabolic syndrome,atherosclerosis, and cardiomyopathy comprises (1) identifying one ormore biochemical pathways associated with one or more biomarkers for ametabolic syndrome-related metabolic disorder selected from therespective table(s) (Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21,22, 23, 25, 26, 27, and/or 28) and one or more non-biomarker compoundsof the disease or condition and (2) identifying a protein affecting atleast one of the one or more identified biochemical pathways, theprotein being a potential drug target for the disease or condition. Forexample, a method for identifying a potential drug target for insulinresistance comprises (1) identifying one or more biochemical pathwaysassociated with one or more biomarkers for insulin resistance selectedfrom Tables 4, 5, 6, 7, 8, 9A, 9B, 27, and/or 28, and one or morenon-biomarker compounds of insulin resistance and (2) identifying aprotein affecting at least one of the one or more identified biochemicalpathways, the protein being a potential drug target for insulinresistance.

One or more biochemical pathways (e.g., biosynthetic and/or metabolic(catabolic) pathway) are identified that are associated with one or morebiomarkers (or non-biomarker compounds). After the biochemical pathwaysare identified, one or more proteins affecting at least one of thepathways are identified. Preferably, those proteins affecting more thanone of the pathways are identified.

A build-up of one metabolite (e.g., a pathway intermediate) may indicatethe presence of a ‘block’ downstream of the metabolite and the block mayresult in a low/absent level of a downstream metabolite (e.g. product ofa biosynthetic pathway). In a similar manner, the absence of ametabolite could indicate the presence of a ‘block’ in the pathwayupstream of the metabolite resulting from inactive or non-functionalenzyme(s) or from unavailability of biochemical intermediates that arerequired substrates to produce the product. Alternatively, an increasein the level of a metabolite could indicate a genetic mutation thatproduces an aberrant protein which results in the over-production and/oraccumulation of a metabolite which then leads to an alteration of otherrelated biochemical pathways and result in dysregulation of the normalflux through the pathway; further, the build-up of the biochemicalintermediate metabolite may be toxic or may compromise the production ofa necessary intermediate for a related pathway. It is possible that therelationship between pathways is currently unknown and this data couldreveal such a relationship.

The proteins identified as potential drug targets may then be used toidentify compositions that may be potential candidates for treating aparticular disease or condition, such as insulin resistance, includingcompositions for gene therapy.

IX. Methods of Treatment

In another aspect, methods for treating a disease or condition such aspre-diabetes, type-2 diabetes, metabolic syndrome, atherosclerosis, andcardiomyopathy are provided. The methods generally involve treating asubject having a disease or condition such as pre-diabetes, type-2diabetes, metabolic syndrome, atherosclerosis, and cardiomyopathy withan effective amount of one or more biomarker(s) that are lowered in asubject having the disease or condition as compared to a healthy subjectnot having the disease or condition. The biomarkers that may beadministered may comprise one or more of the biomarkers in Tables 4, 5,6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, 28, and/or 29that are decreased in a disease or condition state as compared tosubjects not having that disease or condition. Such biomarkers could beisolated based on the identity of the biomarker compound (i.e. compoundname). The biomarkers that are currently unnamed metabolites could beisolated based on the analytical characterizations for the biomarkerslisted in the Examples below (e.g. Table 29). In some embodiments, thebiomarkers that are administered are one or more biomarkers listed inTables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27,28, and/or 29 that are decreased in a metabolic syndrome-relatedmetabolic disorder and that have a p-value less than 0.05 or a q-valueless than 0.10, or both a p-value less than 0.05 and a q-value less than0.10, as determined by using a Welch's T-test or a Wilcoxon's rank sumTest. In other embodiments, the biomarkers that are administered are oneor biomarkers listed in Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17,21, 22, 23, 25, 26, 27, 28, and/or 29 that are decreased in a disease orcondition and that have either a positive or negative correlation with adisease or condition. In one embodiment, the biomarkers have a positiveor negative correlation either ≧+0.5 or ≦−0.5, respectively, with adisease or condition. In other embodiments, the biomarkers that areadministered are one or more biomarkers listed in Tables 4, 5, 6, 7, 8,9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, 28, and/or 29 that aredecreased in a disease or condition by at least 5%, by at least 10%, byat least 15%, by at least 20%, by at least 25%, by at least 30%, by atleast 35%, by at least 40%, by at least 45%, by at least 50%, by atleast 55%, by at least 60%, by at least 65%, by at least 70%, by atleast 75%, by at least 80%, by at least 85%, by at least 90%, by atleast 95%, or by 100% (i.e., absent). In one example, the identificationof biomarkers for insulin resistance also allows for the treatment ofinsulin resistance. For example, in order to treat a subject havinginsulin resistance, an effective amount of one or more insulinresistance biomarkers that are lowered in subjects having insulinresistance as compared to a healthy subject not having insulinresistance may be administered to the subject. The biomarkers that maybe administered may comprise one or more of the biomarkers in Tables 4,5, 6, 7, 8, 9A, 9B, 27, 28, and/or 29 that are decreased in a subjecthaving insulin resistance. Although insulin resistance is discussed inthis example, the other diseases or conditions, such as type-2 diabetes,metabolic syndrome, atherosclerosis, and cardiomyopathy, may also betreated in accordance with this method by using one or more of therespective biomarkers as set forth above.

X. Methods of Using the Biomarkers for Other Diseases or Conditions

In another aspect, at least some of the biomarkers disclosed herein fora particular disease or condition may also be biomarkers for otherdiseases or conditions. For example, it is believed that at least someof the insulin resistance biomarkers may be used in the methodsdescribed herein for other diseases or conditions (e.g., metabolicsyndrome). That is, the methods described herein with respect to insulinresistance may also be used for diagnosing (or aiding in the diagnosisof) a disease or condition such as type-2 diabetes, metabolic syndrome,atherosclerosis, or cardiomyopathy, methods of monitoringprogression/regression of such a disease or condition, methods ofassessing efficacy of compositions for treating such a disease orcondition, methods of screening a composition for activity in modulatingbiomarkers associated with such a disease or condition, methods ofidentifying potential drug targets for such diseases and conditions, andmethods of treating such diseases and conditions. Such methods could beconducted as described herein with respect to insulin resistance.

XI. Other methods

Other methods of using the biomarkers discussed herein are alsocontemplated. For example, the methods described in U.S. Pat. No.7,005,255 and U.S. patent application Ser. Nos. 11/357,732, 10/695,265(Publication No. 2005/0014132), Ser. No. 11/301,077 (Publication No.2006/0134676), Ser. No. 11/301,078 (Publication No. 2006/0134677), Ser.No. 11/301,079 (Publication No. 2006/0134678), and Ser. No. 11/405,033may be conducted using a small molecule profile comprising one or moreof the biomarkers disclosed herein.

In any of the methods listed herein, the biomarkers that are used may beselected from those biomarkers in Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15,16, 17, 21, 22, 23, 25, 26, 27, 28, and/or 29 having p-values of lessthan 0.05 and/or those biomarkers in Tables 4, 5, 6, 7, 8, 9A, 9B, 14,15, 16, 17, 21, 22, 23, 25, 26, 27, 28, and/or 29 having q-values lessthan 0.10, and/or having a positive or negative correlation either ≧+0.5or ≦−0.5, respectively, with a disorder. The biomarkers that are used inany of the methods described herein may also be selected from thosebiomarkers in Tables 4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23,25, 26, 27, 28, and/or 29 that are decreased in a metabolicsyndrome-related metabolic disorder (as compared to the control orremission) or that are decreased in remission (as compared to control ora particular disease or condition) by at least 5%, by at least 10%, byat least 15%, by at least 20%, by at least 25%, by at least 30%, by atleast 35%, by at least 40%, by at least 45%, by at least 50%, by atleast 55%, by at least 60%, by at least 65%, by at least 70%, by atleast 75%, by at least 80%, by at least 85%, by at least 90%, by atleast 95%, or by 100% (i.e., absent); and/or those biomarkers in Tables4, 5, 6, 7, 8, 9A, 9B, 14, 15, 16, 17, 21, 22, 23, 25, 26, 27, 28,and/or 29 that are increased in a given disease or condition (ascompared to the control or remission) or that are increased in remission(as compared to the control or a given disease or condition) by at least5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%,by at least 30%, by at least 35%, by at least 40%, by at least 45%, byat least 50%, by at least 55%, by at least 60%, by at least 65%, by atleast 70%, by at least 75%, by at least 80%, by at least 85%, by atleast 90%, by at least 95%, by at least 100%, by at least 110%, by atleast 120%, by at least 130%, by at least 140%, by at least 150%, ormore.

EXAMPLES I. General Methods

A. Identification of Metabolic Profiles

Each sample was analyzed to determine the concentration of severalhundred metabolites. Analytical techniques such as GC-MS (gaschromatography-mass spectrometry) and LC-MS (liquid chromatography-massspectrometry) were used to analyze the metabolites. Multiple aliquotswere simultaneously, and in parallel, analyzed, and, after appropriatequality control (QC), the information derived from each analysis wasrecombined. Every sample was characterized according to several thousandcharacteristics, which ultimately amount to several hundred chemicalspecies. The techniques used were able to identify novel and chemicallyunnamed compounds.

B. Statistical Analysis:

The data was analyzed using several statistical methods to identifymolecules (either known, named metabolites or unnamed metabolites)present at differential levels in a definable population orsubpopulation (e.g., biomarkers for metabolic syndrome biologicalsamples compared to control biological samples or compared to patientsin remission from insulin resistance) useful for distinguishing betweenthe definable populations (e.g., insulin resistance and control, insulinresistance and remission, remission and control). Other molecules(either known, named metabolites or unnamed metabolites) in thedefinable population or subpopulation were also identified.

Random forest analyses were used for classification of samples intogroups (e.g. disease or healthy, insulin resistant or normal insulinsensitivity, atherosclerosis or normal, metabolic syndrome or obese butnot metabolic syndrome). Random forests give an estimate of how well wecan classify individuals in a new data set into each group, in contrastto a t-test, which tests whether the unknown means for two populationsare different or not. Random forests create a set of classificationtrees based on continual sampling of the experimental units andcompounds. Then each observation is classified based on the majorityvotes from all the classification trees.

Regression analysis was performed using the Random Forest Regressionmethod and the Univariate Correlation/Linear Regression method to buildmodels that are useful to identify the biomarker compounds that areassociated with disease or disease indicators (e.g. atherosclerosis,metabolic syndrome, Rd) and then to identify biomarker compounds usefulto classify individuals according to for example, the level of glucoseutilization as normal, insulin impaired, or insulin resistant. Biomarkercompounds that are useful to predict disease or measures of disease(e.g. atherosclerosis, metabolic syndrome, Rd) and that are positivelyor negatively correlated with disease or measures of disease (e.g.atherosclerosis, metabolic syndrome, Rd) were identified in theseanalyses. All of the biomarker compounds identified in these analyseswere statistically significant (p<0.05, q<0.1).

Recursive partitioning relates a ‘dependent’ variable (Y) to acollection of independent (‘predictor’) variables (X) in order touncover—or simply understand—the elusive relationship, Y=f(X). Theanalysis was performed with the JMP program (SAS) to generate a decisiontree. The statistical significance of the “split” of the data can beplaced on a more quantitative footing by computing p-values, whichdiscern the quality of a split relative to a random event. Thesignificance level of each “split” of data into the nodes or branches ofthe tree was computed as p-values, which discern the quality of thesplit relative to a random event. It was given as LogWorth, which is thenegative log 10 of a raw p-value.

Statistical analyses were performed with the program “R” available onthe worldwide web at the website cran.r-project.org and in JMP 6.0.2(SAS® Institute, Cary, N.C.).

Example 2 Biomarkers of Pre-Diabetes

2A: Identification of Biomarkers that Correlate with Glucose Disposal

A combination of biomarkers were discovered that, when used in analgorithm, correlate with the glucose disposal rate (i.e. Rd). Further,the initial panel of biomarkers can be narrowed for the development oftargeted assays comprised of 15-30 candidate metabolites. An algorithmto predict insulin resistance was developed.

Several studies were conducted to identify biomarkers that correlatewith glucose disposal. In a first study, plasma samples were collectedfrom 113 lean, obese or diabetic subjects that had received treatmentwith one of three different thiazolidinedione drugs (T=troglitazone,R=rosiglitazone, or P=pioglitazone) (Table 1). Base line samplesobtained from the subjects prior to treatment (S=baseline) served ascontrols.

One to three plasma samples were obtained from each subject, withsamples collected at baseline (all subjects; A), and after 12 weeks (B)or 4 weeks (C) of drug treatment (Table 2). Glucose disposal rate wasmeasured in every subject by the hyperinsulinemic euglycemic (HI) clampfollowing each blood draw. A total of 198 plasma samples were collectedfor analysis.

TABLE 1 Gender and treatments of the study 1 cohort. Group Gender P R ST Total Lean F 1 0 1 1 3 M 7 0 12 8 27 Obese F 2 0 3 1 6 M 7 0 14 8 29Diabetic F 0 7 3 1 11 M 8 13 7 9 37 Total 25 20 40 28 113

TABLE 2 Treatment and collection time of the study 1 cohort. GROUP TIMEP R S T Total L A 8 0 13 9 30 B 8 0 0 8 16 O A 9 0 17 9 35 B 9 0 0 9 18C 9 0 0 0 9 D A 8 19 10 9 46 B 8 20 0 10 38 C 6 0 0 0 6 Total 65 39 4054 198

In a second study, plasma samples were collected from 402 subjects thatwere balanced for age and gender. The subjects underwent HI clamp todetermine the glucose disposal rate (Rd) of each individual. Based uponan Oral Glucose Tolerance Test (OGTT) or a Fasting Plasma Glucose Test(FPGT) the glucose tolerance of the subjects was designated as Normalglucose tolerance (NGT), Impaired Fasting Glucose (IFG) or ImpairedGlucose Tolerance (IGT). The cohort is described in Table 3.

TABLE 3 Cohort Description, Study 2 Age Rd Group Gender N Mean Std DevMean Std Dev NGT female 155 44.64 8.02 8.5 3.09 male 148 44.03 8.62 8.382.77 IFG female 5 46.8 6.53 6.13 3.32 male 12 45.25 9.63 4.67 2.57 IGTfemale 45 45.56 7.81 4.19 1.81 male 37 45.73 7.8 4.73 2.27 AbbreviationsRd: Glucose disposal rate NGT: Normal Glucose Tolerant (OGTT, <140 mg/dLor <7.8 mmol/L) IFG: Impaired Fasting Glucose (Fasting plasma glucose,100-125 mg/dL or 5.6-6.9 mmol/L) IGT: Impaired Glucose Tolerant (OGTT,140-199 mg/dL or 7.8-11.0 mmol/L)

Abbreviations

Rd: Glucose disposal rate

NGT: Normal Glucose Tolerant (OGTT, <140 mg/dL or <7.8 mmol/L)IFG: Impaired Fasting Glucose (Fasting plasma glucose, 100-125 mg/dL or5.6-6.9 mmol/L)IGT: Impaired Glucose Tolerant (OGTT, 140-199 mg/dL or 7.8-11.0 mmol/L)

All samples from both studies were analyzed by GC-MS and LC-MS toidentify and quantify the small molecules present in the samples. Over400 compounds were detected in the samples.

Statistical analyses were performed to determine the compounds that areuseful as biomarkers. Linear regression was used to correlate thebaseline levels of individual compounds with the glucose disposal rate(Rd) as measured by the euglycemic hyperinsulinemic clamp for eachindividual. This analysis was followed by Random Forest analysis toidentify variables most useful for Rd modeling. Then, LASSO regressionanalysis was performed on the cross-validated variables from the RandomForest analysis to pick the combination of variables useful to predictRd.

2B: Biomarkers of Glucose Utilization, Molecules Positively andNegatively Correlated with Glucose Disposal Rate (Rd)

Biomarkers were discovered by (1) analyzing blood samples drawn fromdifferent groups of human subjects to determine the levels ofmetabolites in the samples and then (2) statistically analyzing theresults to determine those metabolites that were differentially presentin the groups of subjects and the metabolites that correlate with theglucose disposal rate, an indicator of insulin sensitivity.

The plasma samples used for the analysis were from the cohorts describedin Tables 1, 2 and 3; the subjects had various rates of glucose disposal(Rd). Based on the Rd value subjects were classified as insulinresistant (Rd≦4), insulin impaired (4<Rd<7.5) or insulin sensitive(Rd>7.5). After the levels of metabolites were determined, the data wasanalyzed using Univariate Correlation/Linear Regression.

As listed below in Table 4, biomarkers were discovered that werecorrelated with the glucose disposal rate (Rd), an indicator of insulinsensitivity.

Table 4 includes, for each listed biomarker, the p-value determined inthe statistical analysis of the data concerning the biomarkers, and thecorrelation (Con) with Rd. A positive correlation indicates that thelevel of the biomarker increases as the glucose disposal rate increases.A negative correlation indicates that the level of the biomarkerdecreases as the glucose disposal rate increases. The range of possiblecorrelations is between negative (−)1.0 and positive (+)1.0. A result ofnegative (−)1.0 means a perfect negative correlation, a positive (+)1.0means a perfect positive correlation, and 0 means no correlation at all.The term “Isobar” as used in the table indicates the compounds thatcould not be distinguished from each other on the analytical platformused in the analysis (i.e., the compounds in an isobar elute at nearlythe same time and have similar (and sometimes exactly the same) quantions, and thus cannot be distinguished).

The results of this analysis showed the individual compounds that arecorrelated with Rd in both study 1 and study 2; these biomarkers arelisted in Table 4. For each biomarker the study, compound name, databaseidentifier, median importance are given. The Library ID (LIB_ID)indicates the analytical platform that was used to measure the biomarkercompound. GC-MS is indicated by Library ID (Lib_ID) 50 whereas LC-MS isindicated by Library ID 61, 200, and 201. The biomarker compounds areordered in the table by the statistical significance of the correlation(P-value). “RF_Rank” refers to the importance score obtained for thebiomarker from Random Forest analysis. “Comp_ID” refers to the internaldatabase identifier for that compound in our internal compound library.

TABLE 4 Biomarker correlation with Rd Study Comp_ID Compound Name Lib_IDRF_Rank Correlation R-square P-value 2 21044 2-hydroxybutyrate (AHB) 501 −4.47E−01 0.200 1.21E−14 1 20488 glucose 50 1 3.69E−01 0.136 1.21E−141 587 gluconate 50 2 −6.11E−01 0.373 8.73E−13 1 1336 palmitate (16:0)201 5 −5.95E−01 0.355 4.37E−12 1 33416 Metabolite-12064 201 9 −5.94E−010.353 5.09E−12 1 20675 1,5-anhydroglucitol (1,5-AG) 201 13 5.93E−010.352 5.62E−12 1 12751 glutamate-2 50 3 −5.82E−01 0.339 1.66E−11 1 1121margarate (17:0) 50 4 −5.52E−01 0.304 2.88E−10 1 584 mannose 50 6−5.50E−01 0.303 3.28E−10 1 31535 Bradykinin, hydroxyproline 61 61.22E−08 0.295 7.09E−10 form 1 21044 2-hydroxybutyrate (AHB) 50 14−5.40E−01 0.292 7.75E−10 1 27719 galactonic acid 50 1 −5.40E−01 0.2918.28E−10 1 16235 Isobar.19 (1,5-AG etc) 61 31 1.96E−08 0.287 1.43E−09 133388 Metabolite-12037 201 19 −5.32E−01 0.283 1.58E−09 2 1359 oleate(18:1(n-9)) 201 3 −3.54E−01 0.125 2.15E−09 1 1358 stearate (18:0) 201 15−5.22E−01 0.273 3.57E−09 1 27392 dipalmitin 50 8 −5.18E−01 0.2684.97E−09 1 32569 Metabolite-11252 200 56 −5.10E−01 0.260 9.09E−09 133232 Metabolite-11887 201 41 −5.03E−01 0.253 1.60E−08 1 210473-methyl-2-oxobutyrate 201 42 −5.03E−01 0.253 1.64E−08 1 10737 Isobar.1(mannose, glucose 61 36 1.72E−07 0.254 1.75E−08 etc) 1 22570Metabolite-9033 50 62 −4.98E−01 0.248 2.24E−08 1 32566 Metabolite-11249200 70 −4.95E−01 0.245 2.82E−08 1 18369 gamma-glutamylleucine 200 12−4.95E−01 0.245 2.95E−08 1 25602 Metabolite-10432 50 39 −4.91E−01 0.2413.79E−08 1 27722 erythrose 50 43 −4.91E−01 0.241 3.84E−08 1 32630 oleate(18:1(n-9)) 201 54 −4.86E−01 0.236 5.47E−08 1 27888 Metabolite-10609 5010 −4.85E−01 0.235 5.87E−08 1 24077 Metabolite-9727 50 22 −4.85E−010.235 6.15E−08 1 32696 Metabolite-11379 201 37 −4.84E−01 0.234 6.50E−081 12666 Threonine 50 9 4.82E−07 0.235 7.02E−08 1 32551 Metabolite-11234201 34 −4.83E−01 0.233 7.11E−08 1 30288 Metabolite-10750 50 25 −4.82E−010.232 7.39E−08 1 33413 Metabolite-12061 201 16 −4.80E−01 0.231 8.27E−081 60 leucine 200 58 −4.80E−01 0.230 8.52E−08 1 21127 palmitoylglycerol50 60 −4.75E−01 0.225 1.23E−07 (monopalmitin) 1 32393 glutamylvaline 20011 −4.74E−01 0.225 1.26E−07 1 19462 Metabolite-6446 50 30 −4.74E−010.225 1.28E−07 1 16120 Metabolite-4055 50 38 −4.73E−01 0.223 1.44E−07 132515 valine 200 71 −4.72E−01 0.223 1.49E−07 1 5628 Metabolite-1086 61 51.12E−06 0.221 1.96E−07 1 32704 Metabolite-11387 200 115 −4.68E−01 0.2191.99E−07 1 32571 Metabolite-11254 200 97 −4.66E−01 0.217 2.25E−07 132501 dihomo-alpha-linolenate 201 26 −4.66E−01 0.217 2.31E−07(20:3(n-3)) 1 17627 Metabolite-4986 50 24 −4.61E−01 0.213 3.05E−07 132402 gondoate (20:1(n-9)) 201 101 −4.48E−01 0.201 7.10E−07 1 32575Metabolite-11258 200 241 −4.47E−01 0.200 7.75E−07 1 32970Metabolite-11653 201 51 −4.45E−01 0.198 8.81E−07 1 16512 Metabolite-427550 54 3.95E−06 0.2 8.91E−07 1 12757 Metabolite-3078 50 52 4.44E−01 0.1979.64E−07 1 27718 creatine 200 27 −4.41E−01 0.194 1.16E−06 1 30832Metabolite-10814 50 47 −4.40E−01 0.193 1.23E−06 1 30290 Metabolite-1075250 121 −4.38E−01 0.192 1.36E−06 1 32703 Metabolite-11386 200 94−4.37E−01 0.191 1.47E−06 1 577 fructose 50 93 −4.36E−01 0.190 1.53E−06 122116 4-methyl-2-oxopentanoate 201 74 −4.35E−01 0.189 1.65E−06 1 33172Metabolite-11827 201 61 4.35E−01 0.189 1.67E−06 1 22600 Metabolite-904350 90 −4.31E−01 0.186 2.05E−06 1 1125 isoleucine 200 137 −4.31E−01 0.1852.14E−06 1 19490 Metabolite-6488 50 32 −4.28E−01 0.183 2.46E−06 1 16518Metabolite-4276 50 18 −4.26E−01 0.182 2.78E−06 1 15122 glycerol 50 45−4.25E−01 0.181 2.93E−06 1 12782 Metabolite-3100 50 17 −4.23E−01 0.1793.39E−06 1 33242 Metabolite-11897 201 77 −4.21E−01 0.177 3.75E−06 117330 Metabolite-4769 50 35 4.21E−01 0.177 3.86E−06 1 32672Metabolite-02546_200 200 67 4.14E−01 0.171 5.69E−06 1 33237Metabolite-11892 201 124 −4.12E−01 0.170 6.48E−06 1 32673 linoleate(18:2(n-6)) 201 28 −4.07E−01 0.166 8.49E−06 1 32545 Metabolite-11228 20164 −4.03E−01 0.163 1.05E−05 1 32751 Metabolite-11434 201 63 −4.03E−010.162 1.06E−05 1 22895 Metabolite-9299 50 72 −3.99E−01 0.159 1.34E−05 233488 5-alpha-Cholest-7-en-3- 50 11 −2.61E−01 0.068 1.40E−05 beta-ol 132517 Metabolite-11203 200 82 3.98E−01 0.158 1.42E−05 1 33415Metabolite-12063 201 44 −3.97E−01 0.158 1.46E−05 1 32682Metabolite-11365 201 29 −3.94E−01 0.155 1.72E−05 2 32761Metabolite-11444 201 12 −2.58E−01 0.067 1.77E−05 2 32338 glycine 50 132.58E−01 0.066 1.83E−05 1 32749 Metabolite-11432 201 107 −3.91E−01 0.1532.04E−05 1 1110 arachidonate (20:4(n-6)) 50 49 −3.90E−01 0.152 2.12E−051 33138 Metabolite-11793 200 96 −3.88E−01 0.150 2.39E−05 2 33447palmitoleate (16:1(n-7)) 201 15 −2.53E−01 0.064 2.59E−05 1 32504 n-3 DPA(22:5(n-3)) 201 31 −3.83E−01 0.147 3.05E−05 1 18868 Metabolite-5847 5080 −3.80E−01 0.144 3.56E−05 1 27738 threonate 50 53 3.75E−01 0.1414.62E−05 1 32552 Metabolite-11235 201 73 −3.75E−01 0.140 4.68E−05 127279 Metabolite-10511 50 7 −3.69E−01 0.136 6.11E−05 1 32547Metabolite-11230 201 163 −3.68E−01 0.135 6.55E−05 1 19377Metabolite-6272 50 194 −3.66E−01 0.134 7.40E−05 1 32416 alpha-linolenate(18:3(n-3)) 201 65 -3.66E−01 0.134 7.41E−05 1 33080 Metabolite-11735 20050 3.63E−01 0.132 8.27E−05 1 22320 Metabolite-8889 50 177 3.62E−01 0.1318.90E−05 1 32945 Metabolite-11628 201 197 −3.60E−01 0.130 9.48E−05 2 599pyruvate 50 18 −2.31E−01 0.053 1.00E−04 2 33453 alpha-ketoglutarate 5017 −2.35E−01 0.055 1.00E−04 2 1105 linoleate (18:2(n-6)) 201 16−2.44E−01 0.059 1.00E−04 1 527 lactate 50 76 −3.58E−01 0.128 1.06E−04 115676 3-methyl-2-oxovalerate 201 216 −3.53E−01 0.125 1.35E−04 1 32836peptide-HWESASXX 200 33 −3.47E−01 0.121 1.76E−04 1 32637Metabolite-11320 201 100 3.45E−01 0.119 1.91E−04 2 15749 hydrocinnamicacid 201 21 2.26E−01 0.051 2.00E−04 2 1648 serine 50 19 2.27E−01 0.0522.00E−04 1 15500 carnitine 200 310 −3.38E−01 0.114 2.64E−04 1 16496pyruvate 50 119 −3.35E−01 0.113 3.00E−04 1 32559 Metabolite-11242 201 87−3.35E−01 0.112 3.11E−04 1 32632 Metabolite-11315 200 110 3.31E−01 0.1103.61E−04 2 33587 Isobar-cis-9-cis-11-trans-11- 201 23 -2.16E−01 0.0464.00E−04 eicosenoate 2 32401 trigonelline (N- 200 22 2.16E−01 0.0474.00E−04 methylnicotinate) 1 21630 Metabolite-8402 50 36 −3.27E−01 0.1074.40E−04 1 16666 Metabolite-4365 50 272 −3.25E−01 0.105 4.79E−04 1 16665Metabolite-4364 50 105 3.24E−01 0.105 4.98E−04 1 19983 Metabolite-695550 122 −3.24E−01 0.105 4.99E−04 2 32405 3-indolepropionate 50 242.10E−01 0.044 5.00E−04 1 31509 Metabolite-10931 50 220 −3.21E−01 0.1035.66E−04 1 27889 Metabolite-10610 50 95 −3.19E−01 0.102 6.12E−04 1 16650Metabolite-4360 50 125 −3.18E−01 0.101 6.37E−04 1 32644 Metabolite-11327200 111 −3.18E−01 0.101 6.39E−04 2 32445 3-methylxanthine 201 262.06E−01 0.042 7.00E−04 1 19370 Metabolite-6268 50 179 3.14E−01 0.0997.47E−04 1 32702 Metabolite-11385 200 221 −3.13E−01 0.098 7.73E−04 119576 Metabolite-6627 50 135 −3.12E−01 0.098 7.97E−04 2 32757Metabolite-11440 201 28 −2.03E−01 0.041 8.00E−04 1 12663 serine-2 50 203.11E−01 0.097 8.44E−04 1 19494 Metabolite-6506 50 236 −3.10E−01 0.0968.82E−04 1 32628 palmitoleate (16:1(n-7)) 201 83 −3.10E−01 0.0968.90E−04 1 59 histidine 201 355 −3.09E−01 0.096 9.00E−04 1 32516Metabolite-11202 200 134 3.08E−01 0.095 9.69E−04 1 33087peptide-RPPGFSPF 200 127 −3.04E−01 0.093 1.11E−03 2 31453 cysteine 50 29−1.95E−01 0.038 1.30E−03 1 16138 Metabolite-4080 50 371 −3.00E−01 0.0901.30E−03 2 24074 Metabolite-9706 50 30 1.94E−01 0.038 1.40E−03 1 32595Metabolite-08993_200 200 48 −2.97E−01 0.088 1.46E−03 1 16509Metabolite-4273 50 369 2.96E−01 0.088 1.50E−03 1 32735Metabolite-01911_200 200 146 −2.96E−01 0.088 1.53E−03 1 30281 glycine-250 154 2.95E−01 0.087 1.58E−03 1 32519 Metabolite-11205 200 120 2.95E−010.087 1.59E−03 2 33531 Metabolite-12116 200 31 1.91E−01 0.036 1.60E−03 164 phenylalanine 200 21 −2.90E−01 0.084 1.89E−03 1 32548Metabolite-11231 201 131 −2.90E−01 0.084 1.89E−03 1 22154 bradykinin 20055 −2.89E−01 0.084 2.00E−03 1 32348 2-aminobutyrate 200 297 −2.86E−010.082 2.25E−03 1 31537 peptide-HWESASXXR 200 99 −2.84E−01 0.081 2.41E−031 32747 Metabolite-01142_201 201 148 −2.83E−01 0.080 2.48E−03 1 32550Metabolite-02272_201 201 176 2.82E−01 0.080 2.59E−03 2 15753 hippurate200 35 1.82E−01 0.033 2.70E−03 2 32198 acetylcarnitine 200 34 −1.82E−010.033 2.70E−03 1 21188 stearoylglycerol 50 196 −2.78E−01 0.077 2.97E−03(monostearin) 1 12626 Metabolite-3003 50 198 2.77E−01 0.077 3.10E−03 132654 Metabolite-11337 200 84 −2.73E−01 0.075 3.53E−03 1 21421Metabolite-8214 50 98 −2.73E−01 0.075 3.58E−03 2 32807 Metabolite-11490201 38 −1.74E−01 0.030 4.10E−03 1 606 uridine 201 230 −2.69E−01 0.0724.16E−03 1 19487 Metabolite-6486 50 78 −2.69E−01 0.072 4.18E−03 1 32412butyrylcarnitine 200 389 −2.69E−01 0.072 4.20E−03 2 32616Metabolite-11299 201 41 1.72E−01 0.030 4.60E−03 1 25459 Metabolite-1039550 285 −2.62E−01 0.068 5.35E−03 1 33210 Metabolite-11865 201 1712.61E−01 0.068 5.47E−03 1 27264 Metabolite-10503 50 181 2.59E−01 0.0675.85E−03 1 32578 Metabolite-11261 200 223 −2.58E−01 0.067 5.93E−03 132609 Metabolite-01345_201 201 57 2.58E−01 0.067 5.94E−03 1 25609Metabolite-10439 50 150 −2.57E−01 0.066 6.19E−03 1 12768 Metabolite-308850 91 2.56E−01 0.065 6.49E−03 1 18120 Metabolite-5348 50 108 −2.56E−010.065 6.53E−03 2 3147 xanthine 50 44 −1.65E−01 0.027 6.60E−03 2 15990glycerophosphorylcholine 200 45 1.64E−01 0.027 6.90E−03 (GPC) 1 2730gamma-glutamylglutamine 200 109 2.51E−01 0.063 7.60E−03 1 32701 urate200 85 −2.50E−01 0.063 7.80E−03 1 33227 Metabolite-11882 201 23−2.49E−01 0.062 8.01E−03 1 19934 inositol 50 227 −2.44E−01 0.0599.62E−03 1 25402 Metabolite-10360 50 129 −2.44E−01 0.059 9.63E−03 132520 Metabolite-11206 200 218 −2.43E−01 0.059 9.91E−03 2 32877Metabolite-11560 201 49 1.56E−01 0.024 1.02E−02 1 32753Metabolite-09789_201 201 193 2.42E−01 0.058 1.03E−02 2 1494 5-oxoproline200 50 1.56E−01 0.024 1.04E−02 1 12774 Metabolite-3094 50 199 −2.41E−010.058 1.05E−02 1 32635 Metabolite-11318 201 175 2.41E−01 0.058 1.05E−021 20950 Metabolite-7846 50 162 −2.40E−01 0.058 1.07E−02 1 32606bilirubin 201 289 2.40E−01 0.058 1.08E−02 1 32752 Metabolite-11435 201184 −2.38E−01 0.057 1.16E−02 1 32754 Metabolite-11437 201 186 2.34E−010.055 1.29E−02 1 12129 beta-hydroxyisovalerate 50 140 −2.34E−01 0.0551.30E−02 1 17028 Metabolite-4611 50 130 −2.34E−01 0.055 1.31E−02 1 33132Metabolite-11787 200 251 −2.34E−01 0.055 1.31E−02 1 12067 undecanoate201 183 −2.33E−01 0.054 1.34E−02 1 542 3-hydroxybutyrate (BHBA) 50 206−2.33E−01 0.054 1.35E−02 2 33323 Metabolite-11977 200 51 −1.49E−01 0.0221.41E−02 1 512 asparagine 50 75 2.31E−01 0.053 1.44E−02 2 54 tryptophan200 52 1.47E−01 0.022 1.53E−02 1 22032 Metabolite-8766 50 208 −2.28E−010.052 1.57E−02 2 32792 Metabolite-11475 201 53 −1.46E−01 0.021 1.64E−021 32625 Metabolite-11308 201 301 −2.26E−01 0.051 1.65E−02 1 32813Metabolite-11496 201 116 −2.25E−01 0.051 1.69E−02 1 18477glycodeoxycholate 201 192 −2.24E−01 0.050 1.76E−02 2 12795Metabolite-3113 50 54 1.44E−01 0.021 1.78E−02 1 32553Metabolite-03832_201 201 256 −2.23E−01 0.050 1.82E−02 2 33364gamma-glutamylthreonine- 200 56 1.43E−01 0.020 1.88E−02 2 2342 serotonin(5HT) 200 57 1.40E−01 0.020 2.12E−02 1 32855 Metabolite-11538 201 462.17E−01 0.047 2.13E−02 1 32197 3-(4-hydroxyphenyl) lactate 201 190−2.14E−01 0.046 2.38E−02 2 2132 citrulline 200 58 1.37E−01 0.0192.41E−02 1 21049 1,6-anhydroglucose 50 136 2.13E−01 0.045 2.43E−02 133362 gamma- 200 173 −2.12E−01 0.045 2.49E−02 glutamylphenylalanine 132452 propionylcarnitine 200 252 −2.12E−01 0.045 2.51E−02 1 32656Metabolite-11339 201 142 2.11E−01 0.045 2.55E−02 2 1365 myristate (14:0)201 59 −1.36E−01 0.018 2.59E−02 1 25532 Metabolite-10413 50 226−2.10E−01 0.044 2.63E−02 2 3141 betaine 200 60 1.35E−01 0.018 2.64E−02 132648 Metabolite-11331 201 132 2.10E−01 0.044 2.66E−02 1 25548Metabolite-10419 50 128 2.08E−01 0.043 2.76E−02 1 32748 Metabolite-11431201 178 −2.08E−01 0.043 2.79E−02 1 33135 Metabolite-11790 200 138−2.07E−01 0.043 2.84E−02 1 31518 Metabolite-10933 50 152 −2.07E−01 0.0432.85E−02 1 19478 Metabolite-6467 50 118 −2.06E−01 0.042 2.95E−02 2 33226Metabolite-11881 201 65 1.32E−01 0.018 2.97E−02 1 32561 Metabolite-11244201 243 −2.05E−01 0.042 2.98E−02 1 11438 phosphate 50 273 −2.05E−010.042 3.02E−02 2 1572 glycerate 50 66 1.31E−01 0.017 3.10E−02 2 33477erythronate- 50 67 1.31E−01 0.017 3.13E−02 1 12781 Metabolite-3099 50141 −2.03E−01 0.041 3.18E−02 1 32732 Metabolite-11415 201 155 2.03E−010.041 3.22E−02 1 1299 tyrosine 200 147 −2.02E−01 0.041 3.31E−02 2 27256Metabolite-10500 50 69 −1.29E−01 0.017 3.37E−02 1 32346glycochenodeoxycholate 201 202 −2.00E−01 0.040 3.45E−02 1 27710N-acetylglycine 50 126 2.00E−01 0.040 3.47E−02 1 22842 cholate 201 165−1.99E−01 0.040 3.56E−02 1 31373 Metabolite-10878 50 274 1.98E−01 0.0393.59E−02 2 16511 Metabolite-4274 50 72 1.27E−01 0.016 3.71E−02 2 15996aspartate 50 73 1.27E−01 0.016 3.72E−02 1 33228 Metabolite-11883 200 3321.97E−01 0.039 3.72E−02 1 18929 Metabolite-5907 50 172 −1.96E−01 0.0393.80E−02 2 569 caffeine 200 74 −1.26E−01 0.016 3.87E−02 2 32971Metabolite-11654 200 76 −1.25E−01 0.016 3.98E−02 1 32795Metabolite-11478 201 189 −1.94E−01 0.038 4.00E−02 1 32868 glycocholate201 225 −1.93E−01 0.037 4.12E−02 2 18335 quinate 50 78 1.24E−01 0.0154.24E−02 1 32587 Metabolite-02249_201 201 89 −1.91E−01 0.036 4.38E−02 122548 Metabolite-9026 50 337 −1.91E−01 0.036 4.39E−02 1 32829Metabolite-03653_200 200 102 1.90E−01 0.036 4.48E−02 1 33185Metabolite-11840 201 104 −1.90E−01 0.036 4.49E−02 1 20699 erythritol 50133 −1.90E−01 0.036 4.50E−02 1 32588 Metabolite-01327_201 201 233−1.90E−01 0.036 4.52E−02 1 32848 Metabolite-11531 201 245 −1.89E−010.036 4.54E−02 1 17389 Metabolite-4796 50 79 1.89E−01 0.036 4.57E−02 21493 ornithine 50 82 1.22E−01 0.015 4.60E−02 2 32418myristoleate*14-1-n-5- 201 84 −1.21E−01 0.015 4.68E−02 2 15140kynurenine 200 83 1.21E−01 0.015 4.68E−02 1 32596 Metabolite-02250_200200 151 −1.88E−01 0.035 4.69E−02 1 33380 Metabolite-12029 201 681.87E−01 0.035 4.78E−02 1 27272 Metabolite-10505 50 393 1.86E−01 0.0354.96E−02 1 63 cholesterol 50 212 −1.86E−01 0.035 4.96E−02 1 33198Metabolite-11853 201 314 −1.84E−01 0.034 5.21E−02 2 15630N-acetylornithine 200 87 1.17E−01 0.014 5.52E−02 2 19323docosahexaenoate 201 90 −1.16E−01 0.013 5.66E−02 (DHA) 22-6-n-3- 2 32760Metabolite-11443 201 91 −1.16E−01 0.013 5.68E−02 2 18392 theobromine 20092 1.16E−01 0.013 5.73E−02 1 32514 Metabolite-11200 200 201 1.79E−010.032 5.88E−02 2 16634 Metabolite-4357 50 96 −1.13E−01 0.013 6.37E−02 132518 Metabolite-11204 200 396 −1.75E−01 0.031 6.46E−02 1 33081Metabolite-11736 200 276 −1.75E−01 0.031 6.47E−02 1 32850Metabolite-11533 201 217 −1.73E−01 0.030 6.82E−02 1 32671Metabolite-11354 200 139 1.73E−01 0.030 6.86E−02 2 32786Metabolite-11469 200 98 1.11E−01 0.012 6.92E−02 2 25522 Metabolite-1040750 99 −1.10E−01 0.012 7.11E−02 2 1649 valine 200 100 −1.10E−01 0.0127.21E−02 1 32684 Metabolite-11367 201 224 −1.70E−01 0.029 7.23E−02 1 553cotinine 200 266 1.70E−01 0.029 7.24E−02 1 33389 Metabolite-12038 201214 −1.70E−01 0.029 7.24E−02 1 1563 chenodeoxycholate 201 370 −1.70E−010.029 7.39E−02 2 22481 Metabolite-8988 50 102 1.09E−01 0.012 7.48E−02 112593 Metabolite-2973 50 149 1.67E−01 0.028 7.76E−02 1 32793Metabolite-11476 200 383 −1.67E−01 0.028 7.80E−02 1 32564Metabolite-11247 201 249 1.67E−01 0.028 7.84E−02 2 1508 pantothenate 200104 −1.07E−01 0.012 7.84E−02 2 16829 Metabolite-4503 50 105 1.07E−010.011 7.99E−02 1 32652 Metabolite-11335 200 388 1.66E−01 0.028 8.03E−021 15365 glycerol 3-phosphate (G3P) 50 348 −1.65E−01 0.027 8.15E−02 232875 Metabolite-11558 200 107 −1.06E−01 0.011 8.29E−02 2 15506 choline200 108 1.06E−01 0.011 8.32E−02 2 32492 caprylate-8-0- 201 109 1.05E−010.011 8.51E−02 1 16287 Metabolite-2800 50 209 −1.63E−01 0.027 8.51E−02 11114 deoxycholate 201 106 −1.63E−01 0.027 8.63E−02 2 32619Metabolite-11302 201 110 1.05E−01 0.011 8.64E−02 1 27275Metabolite-10507 50 182 1.62E−01 0.026 8.74E−02 1 32718Metabolite-01342_200 200 167 −1.61E−01 0.026 8.94E−02 2 33403Metabolite-12051 200 112 −1.03E−01 0.011 8.96E−02 1 32769Metabolite-11452 201 278 −1.59E−01 0.025 9.31E−02 2 32756Metabolite-02276_201 201 116 1.01E−01 0.010 9.72E−02 2 33225Metabolite-11880 201 118 −1.01E−01 0.010 9.88E−02 1 32839Metabolite-11522 201 144 1.57E−01 0.025 9.89E−022C: Variable Selection with Random Forest for RD Modeling

50 iterations of a random forest analysis with complete 5-foldcross-validation regressions (for Study 1 this analysis included onlybaseline data, n=111; while for Study 2 all samples were included,n=402) was carried out as follows:

80% of the data was used as the training set to run 1000 regressionrandom forests, record the importance scores and rank the variablesaccording to their importance scores;

Next, four variables at a time were deleted starting from the lowestranked variables, then the random forest was run with the remainingvariables on the training set to predict the remaining 20% of the data(i.e., test set). The error and R-square for each was recorded.

For each variable, the mean/median importance score and rank across allrun was calculated.

Variable selection is more or less stable for the approximately first30-60 variables.

2D: Estimate of the Number of Metabolites Considered Significant for RdCorrelation

The mean R-square values remain constantly high and the correspondingerrors remain consistently low as the number of metabolites reachesapproximately 30 or more (FIGS. 1 and 2), suggesting that a total ofapproximately 30 metabolites may be sufficient for construction of analgorithm to correlate with Rd, although it may also be possible toconstruct an algorithm to correlate with Rd based on a combination ofless than seven metabolites. As a result, only the top 30 to 50cross-validated compounds were selected for regression analyses.

Based on random forest variable selection procedures, the biomarkercompounds that are considered reliably significant for construction ofan algorithm for Rd correlation were identified. The RF score for eachof the biomarker compounds is listed in the column headed “RF_Rank” inTable 4.

2E: Modeling of Rd Correlation with Top Compounds

Based on the modeling experiments, the mean R-square values remainconstantly high and the corresponding errors remain consistently low asthe number of metabolites reaches seven and above (FIGS. 3 and 4),suggesting that a combination of seven metabolites will be sufficientfor construction of an algorithm to correlate with Rd, although it mayalso be possible to construct an algorithm to correlate with Rd based ona combination of less than seven metabolites.

2F: LASSO Regression

Only cross-validated variables from the random forest analyses abovewere used for LASSO regression to pick the best combination of variablesto predict Rd. The most appropriate transformation of thecross-validated variables was also considered for the LASSO regression.

LASSO regression analysis based upon the cohort in study 1 provided oneof the best models of Rd regression with three to nine variables andcross-validated r-square values for the correlation. The best r-squarevalue approaches 0.68 with seven to eight metabolites using thenon-transformed data (Table 5) and approaches 0.69-0.70 with the samenumber of metabolites with appropriate transformation of each variable(Table 6).

TABLE 5 LASSO regression with non-transformed data. Number of Variables3 4 5 6 7 8 9 LASSO 0.355 0.422 0.600 0.643 0.670 0.720 0.779 MaximumR-square Cross- 0.586 0.600 0.653 0.651 0.681 0.687 0.665 ValidatedR-square 1,5-Anhydro- √ √ √ √ √ √ √ D-glucitol Bradykinin- √ √ √ √ √ √ √hydroxyproline Palmitate √ √ √ √ √ √ Metabolite-9727 √ √ √ √ √ Glu-Val √√ √ √ √ Threonine √ √ √ √ Dihydroimidazole- √ √ √ 4-acetate Mannose √2-Hydroxybutyrate √ Isobar-56** √ Serine √ Note: **Isobar 56 includesDL-pipecolic acid and 1-amino-1-cyclopentanecarboxylic acid that can beseparated

TABLE 6 LASSO regression with transformed data. Number of Variables 3 45 6 7 8 9 LASSO Maximum R-square 0.355 0.422 0.600 0.643 0.670 0.7200.779 Cross-Validated R-square 0.592 0.603 0.662 0.657 0.692 0.702 0.6841,5-Anhydro-D-glucitol √ √ √ √ √ √ √ Bradykinin-hydroxyproline √ √ √ √ √√ √ form Log (Palmitate) √ √ √ √ √ √ √ Log (Metabolite-9727) √ √ √ √ √Glu-Val √ √ √ √ √ Threonine √ √ √ √ Log(Dihydroimidazole-4- √ √ √acetate) Log (Mannose) √ Log (2-Hydroxybutyrate) √ Isobar-56** √ Serine√ Note: **Isobar 56 includes DL-pipecolic acid and1-amino-l-cyclopentanecarboxylic

Note: Isobar 56 includes DL-pipecolic acid and1-amino-1-cyclopentanecarboxylic

The R-square for the correlation of Rd with 7-8 metabolites approaches0.70 with cross-validation in an independent cohort.

LASSO analysis based on the cohort in study 1 provided the best modelsof Rd regression with 3-9 variables with cross-validated r-square valuesfor the correlation of another set approaching 0.68 with 7-8 metabolitesusing the non-transformed data as shown in Table 7.

TABLE 7 LASSO regression with non-transformed data. Number of Variables3 4 5 6 7 8 9 Cross-Validated 0.617 0.636 0.650 0.656 0.678 0.681 0.685R-square 1,5-Anhydro- ✓ ✓ ✓ ✓ ✓ ✓ ✓ D-glucitol Palmitate ✓ ✓ ✓ ✓ ✓ ✓ ✓Glu-Val ✓ ✓ ✓ ✓ ✓ ✓ ✓ Serine ✓ ✓ ✓ ✓ ✓ ✓ Margarate ✓ ✓ ✓ ✓ ✓ X-9727 ✓ ✓✓ ✓ X-10511 ✓ ✓ Etio cholanolone ✓ ✓ sulfate (X-1345) Gamma tocopherol ✓(X-4276) Creatine ✓

2G: Models Predictive of Insulin Resistance.

In study 2, compounds identified as important in building models topredict Rd by Random Forest and Lasso Regression are listed in Table 4.The cross-validated compounds were then selected for regression analysisalong with clinical measurements (e.g. fasting insulin, fastingpro-insulin, fasting free fatty acids (FFA), fasting C-peptide, HDLcholesterol, LDL cholesterol, fasting plasma glucose, adiponectin, BMI,PYY, etc.) Each regression method and the Univariate Correlation/LinearRegression method model was then used to predict Rd for each individual,which was in turn used to classify individuals according to the level ofglucose utilization as normal, insulin impaired, or insulin resistant.Samples from ninety percent of the subjects were used to build the modeland samples from the remaining ten percent of the subjects were used totest the predictive power of the model. Biomarker compounds that areuseful to predict Rd and that are positively or negatively correlatedwith Rd were identified in these analyses. These markers are useful topredict insulin resistance. All of the biomarker compounds arestatistically significant (p<0.05) in each of the regression models.

The models generated using this analytical approach are summarized inTable 8. The sensitivity, specificity and predictive power (positive,PPV and negative, NPV) of the models are shown in Table 8. Thesensitivity of the models ranges from about 54% to about 63% and thespecificity ranges from about 63% to greater than 95%. The PPV range isfrom about 78% to about 94% and the NPV from greater than about 79% togreater than about 83%.

TABLE 8 Metabolite Biomarkers and models that are predictive of InsulinResistance as determined by glucose disposal rate (Rd). Model No. No.Variables R-square Sensitivity Specificity PPV NPV Variable 1 Variable 2Variable 3 Variable 4 1 8 0.5486 59.84 95.51 87.36 82.11 BMI FastingInsulin Fasting_Proinsulin Fasting FFA 2 9 0.4937 55.12 95.51 86.4280.41 Fasting Oleate BMI LDL_Cholesterol Insulin 3 7 0.5398 59.06 95.1086.21 81.75 BMI Fasting Insulin Fasting Fasting FFA Proinsulin 4 90.5137 56.69 95.10 85.71 80.90 BMI Fasting Insulin 2HydroxybutyrateGlul.Val. 5 9 0.5308 60.63 95.10 86.52 82.33 Fasting Fasting Glul.ValBMI Insulin Proinsulin 6 7 0.5122 56.69 94.69 84.71 80.84 BMI FastingInsulin Pyruvate Betaine 7 8 0.5179 56.69 94.69 84.71 80.84 BMI FastingInsulin Pyruvate .Gamma.Glu. Leu 8 9 0.5179 56.69 94.69 84.71 80.84 BMIFasting Insulin Lactate Pyruvate 9 7 0.5380 62.20 94.69 85.87 82.86 BMIFasting Insulin Fasting Fasting FFA Proinsulin 10 7 0.5458 61.42 94.6985.71 82.56 BMI Fasting Insulin Fasting Fasting FFA Proinsulin 11 80.5531 60.63 94.69 85.56 82.27 BMI Fasting Insulin Fasting Fasting FFAProinsulin 12 8 0.5534 59.84 94.69 85.39 81.98 BMI Fasting InsulinFasting Fasting FFA Proinsulin 13 9 0.5596 62.99 94.69 86.02 83.15 BMIFasting Insulin Fasting Fasting FFA Proinsulin 14 9 0.5584 59.84 94.6985.39 81.98 BMI Fasting Insulin Fasting Fasting FFA Proinsulin 15 90.5580 59.84 94.69 85.39 81.98 BMI Fasting Insulin Fasting.LDL_Cholesterol Proinsulin 16 9 0.5223 56.69 94.69 84.71 80.84 BMIFasting Insulin 2Hydroxybutyrate Gamma.Glu. Leu 17 9 0.5317 60.63 94.6985.56 82.27 Fasting Glul.Val BMI Betaine Insulin 18 9 0.5106 57.48 94.2993.91 81.05 BMI Fasting Insulin 2Hydroxybutyrate Pyruvate 19 9 0.516755.91 94.29 83.53 80.49 BMI Fasting Insulin 2Hydroxybutyrate Betaine 209 0.5368 61.42 94.29 84.78 82.50 Fasting Fasting BMI Betaine InsulinProinsulin 21 9 0.5253 59.84 94.29 84.44 81.91 Creatine Glycine FastingFasting Insulin Proinsulin 22 9 0.5260 58.27 94.29 84.09 81.34 FastingFasting Pyruvate BMI Insulin Proinsulin 23 9 0.4943 56.69 94.29 83.7280.77 Fasting BMI FPG Linoleate Insulin 24 9 0.4864 55.12 94.29 83.3380.21 Fasting BMI FPG Galactonate Insulin 25 9 0.4958 54.33 94.29 83.1379.93 Triglycerides Linolenate Fasting Lactate Insulin 26 7 0.5401 57.4894.29 83.91 81.05 BMI Fasting Insulin Fasting FFA Adiponectin 27 80.5499 59.06 94.29 84.27 81.63 BMI Fasting Insulin Fasting .FPGProinsulin 28 8 0.5492 59.06 94.29 84.27 81.63 BMI Fasting InsulinFasting .LDL_Cholesterol Proinsulin 29 9 0.5578 62.99 94.29 85.11 83.09BMI Fasting Insulin Fasting Fasting FFA Proinsulin 30 9 0.5576 62.9994.29 85.11 83.09 BMI Fasting Insulin Fasting .FPG Proinsulin 31 90.5615 59.84 94.29 84.44 81.91 BMI Fasting Insulin Fasting Fasting FFAProinsulin 32 9 0.4972 60.63 93.88 83.70 82.14 BMI Fasting Insulin2Hydroxybutyrate Gluconate 33 9 0.5060 55.91 93.88 82.56 80.42 BMIFasting Insulin 2Hydroxybutyrate Glutamate 34 9 0.5635 62.99 93.88 84.2183.03 Fasting Fasting Gamma.Glu. Linoleate Insulin Proinsulin Leu 35 90.4996 55.12 93.88 82.35 80.14 Fasting Fasting_C_Peptide Lactate BMIInsulin 36 9 0.4983 54.33 93.88 82.14 79.86 Fasting Lactate BMILDL_Cholesterol Insulin 37 7 0.5136 55.12 93.88 82.35 80.14 BMI FastingInsulin .Betaine .Gamma.Glu. Leu 38 8 0.5177 55.91 93.88 82.56 80.42 BMIFasting Insulin Pyruvate .Betaine 39 8 0.5183 54.33 93.88 82.14 79.86BMI Fasting Insulin .Betaine .Gamma.Glu. Leu 40 9 0.5177 55.91 93.8882.56 80.42 BMI Fasting Insulin .Galactonate .Gamma.Glu. Leu 41 9 0.518354.33 93.88 82.14 79.86 BMI Fasting Insulin Pyruvate .Gamma.Glu. Leu 427 0.5390 62.99 93.88 84.21 83.03 BMI Fasting Insulin Fasting Fasting FFAProinsulin 43 7 0.5382 62.20 93.88 84.04 82.73 BMI Fasting InsulinFasting Fasting FFA Proinsulin 44 8 0.5479 61.42 93.88 83.87 82.44 BMIFasting Insulin Fasting Fasting FFA Proinsulin 45 8 0.5513 59.84 93.8883.52 81.85 BMI Fasting Insulin Fasting Fasting FFA Proinsulin 46 90.5595 61.42 93.88 83.87 82.44 BMI Fasting Insulin Fasting Fasting FFAProinsulin 47 9 0.5575 59.06 93.88 83.33 81.56 BMI Fasting InsulinFasting Fasting FFA Proinsulin 48 9 0.4936 56.69 93.47 81.82 80.63 BMIFasting Insulin 2Hydroxybutyrate Linolenate 49 9 0.5198 55.91 93.4781.61 80.35 BMI Fasting Insulin 2Hydroxybutyrate Linolyl.LPC 50 9 0.521158.27 93.47 82.22 81.21 Fasting Glutamate BMI 2Hydroxybutyrate Insulin51 9 0.5167 58.27 93.47 82.22 81.21 Fasting Oleoyl.LPC Pyruvate BMIInsulin 52 7 0.5148 57.48 93.47 82.02 80.92 BMI Fasting Insulin.Gamma.Glu. 2Hydroxybutyrate Leu 53 7 0.5128 55.91 93.47 81.61 80.35 BMIFasting Insulin .Glul.Val .Betaine 54 7 0.5139 55.12 93.47 81.40 80.07BMI Fasting Insulin .Gamma.Glu. 2Hydroxybutyrate Leu 55 8 0.5188 57.4893.47 82.02 80.92 BMI Fasting Insulin .Betaine .Gamma.Glu. Leu 56 80.5194 55.91 93.47 81.61 80.35 BMI Fasting Insulin .Gamma.Glu.2Hydroxybutyrate Leu 57 8 0.5178 54.33 93.47 81.18 79.79 BMI FastingInsulin .Betaine .Gamma.Glu. Leu 58 9 0.5188 57.48 93.47 82.02 80.92 BMIFasting Insulin .Betaine .Gamma.Glu. Leu 59 9 0.5194 55.91 93.47 81.6180.35 BMI Fasting Insulin .Betaine .Gamma.Glu. Leu 60 9 0.5178 54.3393.47 81.18 79.79 BMI Fasting Insulin Pyruvate .Betaine 61 7 0.539662.99 93.47 83.33 82.97 BMI Fasting Insulin Fasting Fasting FFAProinsulin 62 8 0.5547 62.99 93.47 83.33 82.97 BMI Fasting InsulinFasting Fasting FFA Proinsulin 63 8 0.5493 62.20 93.47 83.16 82.67 BMIFasting Insulin Fasting Fasting FFA Proinsulin 64 8 0.5525 60.63 93.4782.80 82.08 BMI Fasting Insulin Fasting Fasting FFA Proinsulin 65 90.5615 62.20 93.47 83.16 82.67 BMI Fasting Insulin Fasting Fasting FFAProinsulin 66 9 0.5589 61.42 93.47 82.98 82.37 BMI Fasting InsulinFasting Fasting FFA Proinsulin 67 7 0.5127 55.12 93.06 80.46 80.00 BMIFasting Insulin .Gamma.Glu. 2Hydroxybutyrate Leu 68 8 0.5179 55.91 93.0680.68 80.28 BMI Fasting Insulin .Betaine .Gamma.Glu. Leu 69 9 0.517955.91 93.06 80.68 80.28 BMI Fasting Insulin .Gamma.Glu. 2HydroxybutyrateLeu 70 7 0.5388 57.48 93.06 81.11 80.85 BMI Fasting Insulin Fasting FFAAdiponectin 71 9 0.5081 59.06 93.06 81.52 81.43 FastingFasting_C_Peptide BMI LDL Insulin Cholesterol 72 7 0.5157 56.69 92.6580.00 80.50 BMI Fasting Insulin .Betaine .Gamma.Glu. Leu 73 7 0.512456.69 92.65 80.00 80.50 BMI Fasting Insulin .Betaine .Galactonate 74 70.5131 55.91 92.65 79.78 80.21 BMI Fasting Insulin .Betaine .Gamma.Glu.Leu 75 7 0.5121 55.91 92.65 79.78 80.21 BMI Fasting Insulin Pyruvate.Gamma.Glu. Leu 76 8 0.5187 58.27 92.65 80.43 81.07 BMI Fasting Insulin.Betaine .Galactonate 77 8 0.5183 55.12 92.65 79.55 79.93 BMI FastingInsulin Pyruvate .Betaine 78 8 0.5176 55.12 92.65 79.55 79.93 BMIFasting Insulin .Betaine .Gamma.Glu. Leu 79 9 0.5187 58.27 92.65 80.4381.07 BMI Fasting Insulin Pyruvate .Betaine 80 9 0.5183 55.12 92.6579.55 79.93 BMI Fasting Insulin .Gamma.Glu. 2Hydroxybutyrate Leu 81 90.5176 55.12 92.65 79.55 79.93 BMI Fasting Insulin .Betaine .Galactonate82 7 0.5383 57.48 92.65 80.22 80.78 BMI Fasting Insulin Fasting FFAAdiponectin 83 9 0.5071 55.91 92.65 79.78 80.21 BMI Fasting Insulin2Hydroxybutyrate Oleoyl.LPC 84 9 0.5137 58.27 92.65 80.43 81.07 FastingOleoyl.LPC BMI Galactonate Insulin 85 9 0.5153 53.54 92.65 79.07 79.37Fasting BMI Galactonate Linoleate Insulin 86 9 0.4879 56.69 92.24 79.1280.43 BMI Fasting Insulin 2Hydroxybutyrate Lactate 87 9 0.5032 55.9191.84 78.02 80.07 Fasting Fasting_C_Peptide Lactate BMI Insulin 88 90.4877 55.91 91.84 78.02 80.07 Fasting Lactate Oleate Serine Insulin 897 0.5390 57.48 91.84 78.49 80.65 Fasting Fasting Insulin Fasting FFAAdiponectin Insulin 90 9 0.5122 52.87 62.63 86.05 81.47 Fasting FastingInsulin 2Hydroxybutyrate Creatine Insulin Model No. Variable 5 Variable6 Variable 7 Variable 8 Variable 9 1 Adiponectin GlulVal Betaine2Hydroxybutyrate 2 FPG Linoleate 2Hydroxybutyrate Linolenate HDLCholesterol 3 Adiponectin GlulVal 2Hydroxybutyrate 4 Creatine PyruvateGluconate Glycine Linolenate 5 Betaine 2Hydroxybutyrate OleateAdiponectin Gluconate 6 .Gamma.Glu. 2Hydroxybutyrate Gluconate Leu 72Hydroxybutyrate .Linolyl.LPC Creatine Gluconate 8 Betaine .Galactonate2Hydroxybutyrate .Linolyl.LPC Gluconate 9 HDL .Gamma.Glu.2Hydroxybutyrate Cholesterol Leu 10 Adiponectin .Gamma.Glu.2Hydroxybutyrate Leu 11 Adiponectin .Gamma.Glu. 2HydroxybutyrateCreatine Leu 12 Adiponectin .Betaine .Gamma.Glu. 2Hydroxybutyrate Leu 13Adiponectin .Betaine .Gamma.Glu. 2Hydroxybutyrate Gluconate Leu 14Adiponectin .Gamma.Glu. 2Hydroxybutyrate Creatine Gluconate Leu 15Fasting FFA Adiponectin .Gamma.Glu. .Linoleate 2Hydroxybutyrate Leu 16lin_Linolyl.LPC Betaine Pyruvate Gluconate X9033 17 Fasting2Hydroxybutyrate Linolyl.LPC Adiponectin Gluconate Proinsulin 18Oleoyl.LPC Gluconate Oleate Galactonate Linoleate 19 Glul.Val CreatineGluconate lin_Glycine X9033 20 Gamma.Glu. 2Hydroxybutyrate OleateAdiponectin Gluconate Leu 21 Glul.Val BMI 2Hydroxybutyrate AdiponectinGluconate 22 FPG 2Hydroxybutyrate Lactate Adiponectin Gluconate 232Hydroxybutyrate Linolenate HDL Hepadecenate Oleate Cholesterol 241.5.Anhydroglucitol 2Hydroxybutyrate Lactate Oleate HDL Cholesterol 25BMI LDL_Cholesterol FPG 1.5.Anhydroglucitol 2Hydroxybutyrate 26.Gamma.Glu. 2Hydroxybutyrate Creatine Leu 27 Fasting FFA Adiponectin.Gamma.Glu. 2Hydroxybutyrate Leu 28 Fasting FFA Adiponectin .Gamma.Glu.2Hydroxybutyrate Leu 29 Adiponectin .Gamma.Glu. .Linoleate2Hydroxybutyrate .Linolenate Leu 30 Fasting FFA Adiponectin .Gamma.Glu..Linoleate 2Hydroxybutyrate Leu 31 Adiponectin .Gamma.Glu. .Linoleate2Hydroxybutyrate Creatine Leu 32 Glycine Lactate Galactonate ThreonineX1.5.Anhydroglucitol 33 Pyruvate Oleoyl.LPC Gluconate GlycineGalactonate 34 BMI Fasting FFA 2Hydroxybutyrate Adiponectin Gluconate 35LDL_Cholesterol FPG Glycine Triglycerides 2Hydroxybutyrate 36 FPGGlycine Triglycerides 1.5.Anhydroglucitol 2Hydroxybutyrate 372Hydroxybutyrate Creatine Gluconate 38 .Galactonate 2Hydroxybutyrate.Linolyl.LPC Gluconate 39 2Hydroxybutyrate .Glycine Creatine Gluconate40 2Hydroxybutyrate .Glycine .Linolyl.LPC Creatine Gluconate 412Hydroxybutyrate .Glycine .Linolyl.LPC Creatine Gluconate 42 .Gamma.Glu.2Hydroxybutyrate Gluconate Leu 43 .Betaine .Gamma.Glu. 2HydroxybutyrateLeu 44 Adiponectin .Glul.Val .Linoleate 2Hydroxybutyrate 45 Adiponectin.Gamma.Glu. 2Hydroxybutyrate .Linolyl.LPC Leu 46 Adiponectin .Betaine.Gamma.Glu. .Linoleate 2Hydroxybutyrate Leu 47 Adiponectin .Betaine.Gamma.Glu. 2Hydroxybutyrate Hepadecenate Leu 48 Oleate HepadecenateLinoleate Serine Threonine 49 Betaine Glul.Val Pyruvate GluconateGalactonate 50 Glycine Oleate Adiponectin Creatine Gluconate 51Galactonate 2Hydroxybutyrate Oleate Adiponectin Gluconate 52.Linolyl.LPC Creatine Gluconate 53 2Hydroxybutyrate .Linolyl.LPCGluconate 54 .Glycine Creatine Gluconate 55 2Hydroxybutyrate.Linolyl.LPC Creatine Gluconate 56 .Glycine .Linolyl.LPC CreatineGluconate 57 2Hydroxybutyrate .Linolyl.LPC Gluconate Threonine 582Hydroxybutyrate .Linolyl.LPC .Oleate Gluconate Hepadecenate 592Hydroxybutyrate .Glycine .Linolyl.LPC Creatine Gluconate 60 .Gamma.Glu.2Hydroxybutyrate .Linolyl.LPC Creatine Gluconate Leu 61 .Gamma.Glu..Linoleate 2Hydroxybutyrate Leu 62 Adiponectin .Gamma.Glu. .Linoleate2Hydroxybutyrate Leu 63 Adiponectin .Gamma.Glu. 2HydroxybutyrateHepadecenate Leu 64 Adiponectin .Gamma.Glu. 2Hydroxybutyrate Gluconate.Leu 65 Adiponectin .Gamma.Glu. .Linoleate 2Hydroxybutyrate Gluconate Leu66 Adiponectin .Gamma.Glu. .Linoleate 2Hydroxybutyrate .Linolyl.LPC Leu67 .Glycine .Linolyl.LPC Creatine 68 2Hydroxybutyrate .Glycine.Linolyl.LPC Gluconate 69 .Glycine .Linolenate .Linolyl.LPC CreatineGluconate 70 .Betaine .Gamma.Glu. 2Hydroxybutyrate Leu 71 Galactonate2Hydroxybutyrate Oleate Adiponectin Gluconate 72 2Hydroxybutyrate.Linolyl.LPC Gluconate 73 .Gamma.Glu. 2Hydroxybutyrate Gluconate Leu 742Hydroxybutyrate .Glycine Gluconate 75 2Hydroxybutyrate .Linolyl.LPCGluconate 76 .Gamma.Glu. 2Hydroxybutrate .Linolyl.LPC Gluconate Leu 77.Gamma.Glu. 2Hydroxybutyrate .Linolyl.LPC Gluconate Leu 782Hydroxybutyrate .Linolenate .Linolyl.LPC Gluconate 79 .Galactonate.Gamma.Glu. 2Hydroxybutyrate .Linolyl.LPC Gluconate Leu 80 .Glycine.Linolyl.LPC Creatine Gluconate Threonine 81 .Gamma.Glu.2Hydroxybutyrate .Linolyl.LPC Creatine Gluconate Leu 82 .Gamma.Glu..Linoleate 2Hydroxybutyrate Leu 83 Gluconate Oleate GalactonateHepadecenate Linoleate 84 Linoleate 2Hydroxybutyrate Oleate AdiponectinGluconate 85 2Hydroxybutyrate Glutamate Oleate Adiponectin Gluconate 86Oleate Arginine Hepadecenate Serine Threonine 87 LDL_CholesterolGalactonate Triglycerides 2Hydroxybutyrate Gluconate 88 BMI Arginine2Hydroxybutyrate HDL Threonine Cholesterol 89 .Gamma.Glu.2Hydroxybutyrate .Linolyl.LPC Leu 90 Glutamate Pyruvate GluconateGlycine Galactonate Abbreviations: BMI, Body Mass Index; FFA, Free FattyAcids; FPG, Fasting Plasma Glucose

2H: Correlated Biomarker Compounds:

Many biomarker compounds were correlated as shown in Tables 9A and 9B.Table 9A contains the pair-wise correlation analysis of biomarkersidentified in Study 1 and Table 9B contains the pair-wise correlationanalysis of biomarkers identified in Study 2. Correlated compounds areoften mutually exclusive in regression models and thus can be used (i.e.substituted for a correlated compound) in different models that hadsimilar prediction powers as those shown in Table 8 above. This aspectis useful when developing biochemical assays that are targeted tospecific biomarkers since certain biomarkers may be more amenable toassay development than other biomarkers.

TABLE 9A Correlated Biomarkers in Study 1. Pairwise Correlation NCorrelation P-value R-square 1,5-anhydroglucitol-1,5 (AG)*Metabolite-11234 112 −0.5242 2.99E−09 0.2748 1,5-anhydroglucitol-1,5(AG) *Metabolite-11249 112 −0.5041 1.46E−08 0.25411,5-anhydroglucitol-1,5 (AG) *Metabolite-11252 112 −0.5114 8.32E−090.2615 1,5-anhydroglucitol-1,5 (AG) *Metabolite-12061 112 −0.55212.78E−10 0.3049 1,5-anhydroglucitol-1,5 (AG) *Metabolite-12064 112−0.5046 1.40E−08 0.2546 2-hydroxybutyrate (AHB)*1,5-anhydroglucitol-1,5(AG) 112 −0.5413 7.19E−10 0.2930 2-hydroxybutyrate (AHB)*2-aminobutyrate112 0.7651 <0.000 0.5854 2-hydroxybutyrate (AHB)*3-hydroxybutyrate(BHBA) 112 0.6517 7.11E−15 0.4247 2-hydroxybutyrate(AHB)*3-methyl-2-oxobutyrate 112 0.6750 2.22E−16 0.45572-hydroxybutyrate (AHB)*3-methyl-2-oxovalerate 112 0.5970 3.71E−120.3565 2-hydroxybutyrate (AHB)*4-methyl-2-oxopentanoate 112 0.65992.44E−15 0.4355 2-hydroxybutyrate (AHB)*creatine 112 0.5123 7.75E−090.2624 2-hydroxybutyrate (AHB)*erythrose 112 0.5156 5.96E−09 0.26592-hydroxybutyrate (AHB)*galactonic acid 112 0.7137 <0.000 0.50942-hydroxybutyrate (AHB)*gluconate 112 0.5427 6.35E−10 0.29452-hydroxybutyrate (AHB)*gondoate-20-1-n-9- 112 0.5765 2.91E−11 0.33232-hydroxybutyrate (AHB)*isoleucine 112 0.6025 2.09E−12 0.36302-hydroxybutyrate (AHB)*leucine 112 0.6472 1.27E−14 0.41882-hydroxybutyrate (AHB)*mannose 112 0.7043 <0.000 0.49602-hydroxybutyrate (AHB)*margarate (17:0) 112 0.5270 2.38E−09 0.27772-hydroxybutyrate (AHB)*palmitate (16:0) 112 0.5191 4.54E−09 0.26942-hydroxybutyrate (AHB)*stearate (18:0) 112 0.5888 8.57E−12 0.34672-hydroxybutyrate (AHB)*uridine 112 0.5282 2.15E−09 0.27902-hydroxybutyrate (AHB)*valine* 112 0.6705 6.66E−16 0.44962-hydroxybutyrate (AHB)*Metabolite-10432 112 0.6826 2.22E−16 0.46602-hydroxybutyrate (AHB)*Metabolite-10752 112 0.5221 3.55E−09 0.27262-hydroxybutyrate (AHB)*Metabolite-11228 112 0.5810 1.87E−11 0.33762-hydroxybutyrate (AHB)*Metabolite-11887 112 0.5314 1.66E−09 0.28242-hydroxybutyrate (AHB)*Metabolite-11897 112 0.5037 1.51E−08 0.25372-hydroxybutyrate (AHB)*Metabolite-12037 112 0.5768 2.82E−11 0.33272-hydroxybutyrate (AHB)*Metabolite-12061 112 0.5053 1.33E−08 0.25532-hydroxybutyrate (AHB)*Metabolite-12064 112 0.8857 <0.000 0.78442-hydroxybutyrate (AHB)*glutamate 112 0.7745 <0.000 0.59982-hydroxybutyrate (AHB)*Metabolite-3100 112 0.5619 1.14E−10 0.31582-hydroxybutyrate (AHB)*Metabolite-4055 112 0.6836 <0.000 0.46722-hydroxybutyrate (AHB)*Metabolite-6488 112 0.5779 2.54E−11 0.33392-hydroxybutyrate (AHB)*Metabolite-6627 112 0.5193 4.44E−09 0.26972-hydroxybutyrate (AHB)*Metabolite-9033 112 0.5608 1.27E−10 0.31452-hydroxybutyrate (AHB)*Metabolite-9043 112 0.5879 9.46E−12 0.34562-hydroxybutyrate (AHB)*Metabolite-9727 112 0.7077 <0.000 0.50093-hydroxybutyrate (BHBA)*glutamate 112 0.5506 3.18E−10 0.30323-methyl-2-oxobutyrate*palmitate (16:0) 112 0.5683 6.31E−11 0.32304-methyl-2-oxopentanoate*palmitate (16:0) 112 0.5424 6.53E−10 0.2942alpha linolenate (18:3(n-3))*dihomo-alpha-linolenate-20-3-n-3- 1120.5295 1.94E−09 0.2804 alpha linolenate (18:3(n-3))*gonodoate-20-1-n-9-112 0.7264 <0.000 0.5277 alpha linolenate (18:3(n-3))*linoleate(18:2(n-6)) 112 0.7877 <0.000 0.6204 alpha linolenate(18:3(n-3))*n-3-DPA-22-5-n-3- 112 0.5722 4.37E−11 0.3274 alphalinolenate (18:3(n-3))*oleate (18:1(n-9)) 112 0.7490 <0.000 0.5610 alphalinolenate (18:3(n-3))*palmitate (16:0) 112 0.7354 <0.000 0.5409 alphalinolenate (18:3(n-3))*palmitoleate (16:1(n-7)) 112 0.6224 2.36E−130.3874 alpha linolenate (18:3(n-3))*stearate (18:0) 112 0.6939 <0.0000.4815 alpha linolenate (18:3(n-3))*Metabolite-11365 112 0.5054 1.32E−080.2555 alpha linolenate (18:3(n-3))*Metabolite-11379 112 0.7245 0 0.5249alpha linolenate (18:3(n-3))*Metabolite-11653 112 0.5578 1.66E−10 0.3112alpha linolenate (18:3(n-3))*Metabolite-11887 112 0.7730 0 0.5975 alphalinolenate (18:3(n-3))*Metabolite-12037 112 0.6665 1.11E−15 0.4443BMI*gamma-glutamylleucine 112 0.5215 3.74E−09 0.2719 BMI*glutamylvaline112 0.5425 6.47E−10 0.2943 bradykinin*bradykinin, hydroxyproline form-112 0.5212 3.83E−09 0.2716 creatine*Metabolite-02546_200 112 −0.58491.27E−11 0.3421 dipalmitin*palmitate (16:0) 112 0.5630 1.04E−10 0.3170erythrose*1,5-anhydroglucitol-1,5 (AG) 112 −0.5099 9.35E−09 0.2600erythrose*galactonic acid 112 0.6691 6.66E−16 0.4476 erythrose*gluconate112 0.6461 1.44E−14 0.4174 erythrose*glutamate 112 0.7334 0 0.5378fructose*galactonic acid 112 0.6151 5.35E−13 0.3784 fructose*gluconate112 0.7100 0 0.5042 fructose*glutamate 112 0.6623 1.78E−15 0.4387galactonic acid*1,5-anhydroglucitol-1,5 (AG) 112 −0.6211 2.77E−13 0.3857galactonic acid*2-aminobutyrate 112 0.5246 2.91E−09 0.2752 galactonicacid*3-methyl-2-oxobutyrate 112 0.5517 2.89E−10 0.3044 galactonicacid*4-methyl-2-oxopentanoate 112 0.5288 2.05E−09 0.2797 galactonicacid*gluconate 112 0.7653 0 0.5857 galactonic acid*gondoate-20-1-n-9-112 0.5981 3.33E−12 0.3577 galactonic acid*isoleucine 112 0.54614.74E−10 0.2982 galactonic acid*mannose 112 0.8354 0 0.6978 galactonicacid*margarate (17:0) 112 0.5473 4.26E−10 0.2995 galactonicacid*palmitate (16:0) 112 0.5279 2.21E−09 0.2787 galactonicacid*stearate (18:0) 112 0.5986 3.16E−12 0.3583 galactonicacid*Metabolite-10360 112 0.5113 8.39E−09 0.2614 galactonicacid*Metabolite-10432 112 0.6635 1.55E−15 0.4403 galactonicacid*Metabolite-10609 112 0.5803 2.01E−11 0.3367 galactonicacid*Metabolite-10750 112 0.6247 1.83E−13 0.3903 galactonicacid*Metabolite-10752 112 0.6833 0 0.4668 galactonicacid*Metabolite-11228 112 0.6299 9.99E−14 0.3968 galactonicacid*Metabolite-11230 112 0.5353 1.19E−09 0.2866 galactonicacid*Metabolite-11234 112 0.7072 0 0.5002 galactonicacid*Metabolite-11235 112 0.5168 5.43E−09 0.2671 galactonicacid*Metabolite-11242 112 0.5837 1.43E−11 0.3407 galactonicacid*Metabolite-11249 112 0.5771 2.72E−11 0.3331 galactonicacid*Metabolite-11252 112 0.6003 2.65E−12 0.3603 galactonicacid*Metabolite-11258 112 0.5045 1.42E−08 0.2545 galactonicacid*Metabolite-11387 112 0.5520 2.82E−10 0.3047 galactonicacid*Metabolite-11432 112 0.6147 5.64E−13 0.3778 galactonicacid*Metabolite-11434 112 0.6344 5.93E−14 0.4025 galactonicacid*Metabolite-11628 112 0.5803 2.01E−11 0.3367 galactonicacid*Metabolite-11887 112 0.5451 5.16E−10 0.2971 galactonicacid*Metabolite-11897 112 0.6229 2.25E−13 0.3880 galactonicacid*Metabolite-12037 112 0.5794 2.18E−11 0.3357 galactonicacid*Metabolite-12061 112 0.7387 0 0.5456 galactonicacid*Metabolite-12064 112 0.6051 1.59E−12 0.3662 galactonicacid*glutamate 112 0.9200 0 0.8464 galactonic acid*Metabolite-3100 1120.5343 1.30E−09 0.2855 galactonic acid*Metabolite-4055 112 0.62921.09E−13 0.3958 galactonic acid*Metabolite-5847 112 0.6075 1.23E−120.3690 galactonic acid*Metabolite-6446 112 0.6367 4.53E−14 0.4053galactonic acid*Metabolite-6488 112 0.5753 3.26E−11 0.3309 galactonicacid*Metabolite-6506 112 0.5094 9.72E−09 0.2595 galactonicacid*Metabolite-9033 112 0.7427 0 0.5517 galactonic acid*Metabolite-9043112 0.5751 3.32E−11 0.3307 galactonic acid*Metabolite-9727 112 0.51954.37E−09 0.2699 gamma-glutamylleucine*glutamylvaline 112 0.9404 0 0.8844gamma-glutamylleucine*peptide-HWESASXX 112 0.7379 0 0.5445gamma-glutamylleucine*peptide-HWESASXXR 112 0.5766 2.87E−11 0.3325gluconate*1,5-anhydroglucitol-1,5 (AG) 112 −0.6774 2.22E−16 0.4588gluconate*mannose 112 0.7419 0 0.5504 gluconate*Metabolite-10432 1120.6367 4.51E−14 0.4054 gluconate*Metabolite-10609 112 0.5759 3.08E−110.3316 gluconate*Metabolite-10610 112 0.5098 9.39E−09 0.2599gluconate*Metabolite-10750 112 0.7024 0 0.4934gluconate*Metabolite-10752 112 0.6996 0 0.4894gluconate*Metabolite-11228 112 0.6652 1.33E−15 0.4425gluconate*Metabolite-11230 112 0.5881 9.21E−12 0.3459gluconate*Metabolite-11231 112 0.5374 1.00E−09 0.2888gluconate*Metabolite-11234 112 0.7041 0 0.4958gluconate*Metabolite-11235 112 0.5807 1.92E−11 0.3372gluconate*Metabolite-11242 112 0.6321 7.75E−14 0.3996gluconate*Metabolite-11249 112 0.5309 1.72E−09 0.2819gluconate*Metabolite-11252 112 0.5145 6.51E−09 0.2647gluconate*Metabolite-11387 112 0.5015 1.78E−08 0.2515gluconate*Metabolite-11432 112 0.5697 5.56E−11 0.3245gluconate*Metabolite-11434 112 0.6693 6.66E−16 0.4480gluconate*Metabolite-11435 112 0.5294 1.96E−09 0.2802gluconate*Metabolite-11628 112 0.5278 2.24E−09 0.2785gluconate*Metabolite-11897 112 0.6460 1.47E−14 0.4173gluconate*Metabolite-12061 112 0.7448 0 0.5548gluconate*Metabolite-12064 112 0.5160 5.78E−09 0.2663gluconate*glutamate 112 0.7895 0 0.6234 gluconate*Metabolite-3100 1120.5649 8.70E−11 0.3191 gluconate*Metabolite-4055 112 0.5887 8.67E−120.3466 gluconate*Metabolite-4986 112 0.5742 3.63E−11 0.3297gluconate*Metabolite-5847 112 0.5735 3.88E−11 0.3289gluconate*Metabolite-6446 112 0.6913 0 0.4779 gluconate*Metabolite-6488112 0.5778 2.55E−11 0.3339 gluconate*Metabolite-9033 112 0.7458 0 0.5562gluconate*Metabolite-9043 112 0.5257 2.64E−09 0.2764glutamylvaline*peptide-HWESASXX 112 0.7816 0 0.6109glutamylvaline*peptide-HWESASXXR 112 0.5946 4.75E−12 0.3536glycerate*threonate 112 0.6260 1.57E−13 0.3919 glycerol*alpha linolenate(18:3(n-3)) 112 0.6345 5.86E−14 0.4026 glycerol*linoleate (18:2(n-6))112 0.7008 0 0.4911 glycerol*oleate (18:1(n-9)) 112 0.7338 0 0.5385glycerol*palmitate (16:0) 112 0.6943 0 0.4821gondoate-20-1-n-9-*linolenate (18:2(n-6)) 112 0.7862 0 0.6181gondoate-20-1-n-9-*oleate (18:1(n-9)) 112 0.8756 0 0.7666gondoate-20-1-n-9-*palmitate (16:0) 112 0.8128 0 0.6607 lactate*pyruvate112 0.7722 0 0.5963 lactate*Metabolite-4357 112 0.6960 0 0.4844lactate*Metabolite-4360 112 0.8576 0 0.7355 lactate*Metabolite-4986 1120.6386 3.60E−14 0.4078 lactate*Metabolite-5348 112 0.5750 3.35E−110.3306 linoleate (18:2(n-6))*n-3-DPA-22-5-n-3- 112 0.5966 3.86E−120.3560 linoleate (18:2(n-6))*oleate (18:1(n-9)) 112 0.8621 0 0.7433linoleate (18:2(n-6))*palmitate (16:0) 112 0.8248 0 0.6803 linoleate(18:2(n-6))*palmitoleate (16:1(n-7)) 112 0.6826 2.22E−16 0.4659linoleate (18:2(n-6))*stearate (18:0) 112 0.7065 0 0.4991 linoleate(18:2(n-6))*Metabolite-11379 112 0.7858 0 0.6175 linoleate(18:2(n-6))*Metabolite-11653 112 0.5638 9.63E−11 0.3179 linoleate(18:2(n-6))*Metabolite-11887 112 0.8301 0 0.6891 linoleate(18:2(n-6))*Metabolite-12037 112 0.7066 0 0.4992mannose*1,5-anhydroglucitol-1,5 (AG) 112 −0.6115 8.00E−13 0.3739mannose*margarate (17:0) 112 0.5273 2.33E−09 0.2780 mannose*glutamate112 0.8909 0 0.7937 margarate (17:0)*creatine 112 0.5567 1.85E−10 0.3099margarate (17:0)*stearate (18:0) 112 0.5258 2.63E−09 0.2765 margarate(17:0)*Metabolite-10750 112 0.5003 1.94E−08 0.2503 margarate(17:0)*Metabolite-12037 112 0.5123 7.75E−09 0.2624 margarate(17:0)*Metabolite-12064 112 0.5374 1.00E−09 0.2888 margarate(17:0)*glutamate 112 0.5499 3.38E−10 0.3024 margarate(17:0)*Metabolite-6446 112 0.5082 1.07E−08 0.2582 margarate(17:0)*Metabolite-9033 112 0.5051 1.35E−08 0.2551 myristate(14:0)*oleate (18:1(n-9)) 112 0.5118 8.05E−09 0.2619 myristate(14:0)*palmitate (16:0) 112 0.6230 2.22E−13 0.3881n-3-DPA-22-5-n-3-*oleate (18:1(n-9)) 112 0.6538 5.55E−15 0.4275n-3-DPA-22-5-n-3-*palmitate (16:0) 112 0.6285 1.18E−13 0.3950 oleate(18:1(n-9))*palmitate (16:0) 112 0.9032 0 0.8158 oleate(18:1(n-9))*palmitoleate (16:1(n-7)) 112 0.7218 0 0.5209 oleate(18:1(n-9))*stearate (18:0) 112 0.7798 0 0.6081 oleate(18:1(n-9))*Metabolite-11252 112 0.5052 1.34E−08 0.2552 oleate(18:1(n-9))*Metabolite-11379 112 0.8566 0 0.7337 oleate(18:1(n-9))*Metabolite-11653 112 0.6108 8.61E−13 0.3731 oleate(18:1(n-9))*Metabolite-11887 112 0.8989 0 0.8081 oleate(18:1(n-9))*Metabolite-12037 112 0.8335 0 0.6947 ornithine*EDTA* 1120.6101 9.31E−13 0.3722 ornithine*Metabolite-10812 112 0.5121 7.86E−090.2623 ornithine*Metabolite-3091 112 0.5529 2.59E−10 0.3057ornithine*Metabolite-3103 112 0.5739 3.73E−11 0.3293ornithine*Metabolite-3108 112 0.5599 1.37E−10 0.3135ornithine*Metabolite-4274 112 0.5198 4.29E−09 0.2702 palmitate(16:0)*palmitoleate (16:1(n-7)) 112 0.7277 0 0.5296 palmitate(16:0)*stearate (18:0) 112 0.8313 0 0.6911 palmitate(16:0)*Metabolite-11252 112 0.5102 9.13E−09 0.2603 palmitate(16:0)*Metabolite-11379 112 0.8594 0 0.7386 palmitate(16:0)*Metabolite-11653 112 0.6739 4.44E−16 0.4542 palmitate(16:0)*Metabolite-11887 112 0.8789 0 0.7724 palmitate(16:0)*Metabolite-12037 112 0.7972 0 0.6356 palmitate(16:0)*Metabolite-12064 112 0.5431 6.13E−10 0.2950 palmitoleate(16:1(n-7))*Metabolite-11379 112 0.8744 0 0.7646 stearate(18:0)*Metabolite-11249 112 0.5247 2.88E−09 0.2753 stearate(18:0)*Metabolite-11252 112 0.5606 1.30E−10 0.3142 stearate(18:0)*Metabolite-11258 112 0.5185 4.74E−09 0.2689 stearate(18:0)*Metabolite-11379 112 0.6025 2.10E−12 0.3630 stearate(18:0)*Metabolite-11653 112 0.5560 1.97E−10 0.3091 stearate(18:0)*Metabolite-11887 112 0.7628 0 0.5818 stearate(18:0)*Metabolite-12037 112 0.7230 0 0.5228 stearate(18:0)*Metabolite-12064 112 0.5859 1.15E−11 0.3433Metabolite-10432*1,5-anhydroglucitol-1,5 (AG) 112 −0.5674 6.88E−110.3220 Metabolite-10432*glutamate 112 0.7402 0 0.5479Metabolite-10609*glutamate 112 0.5732 3.99E−11 0.3285glycine*Metabolite-3003 112 0.9861 0 0.9725Metabolite-10750*1,5-anhydroglucitol-1,5 (AG) 112 −0.5347 1.26E−090.2859 Metabolite-10750*glutamate 112 0.6864 0 0.4711Metabolite-10752*1,5-anhydroglucitol-1,5 (AG) 112 −0.5387 8.96E−100.2902 Metabolite-10752*glutamate 112 0.7503 0 0.5629Metabolite-10814*glutamate 112 0.5121 7.85E−09 0.2623serine*Metabolite-3078 112 0.6279 1.26E−13 0.3943 serine*Metabolite-3088112 0.5175 5.15E−09 0.2678 serine*Metabolite-4364 112 0.5899 7.72E−120.3480 serine*Metabolite-4769 112 0.6668 1.11E−15 0.4447glutamate*1,5-anhydroglucitol-1,5 (AG) 112 −0.6945 0 0.4823glutamate*2-aminobutyrate 112 0.5296 1.92E−09 0.2805glutamate*3-methyl-2-oxobutyrate 112 0.5857 1.18E−11 0.3430glutamate*4-methyl-2-oxopentanoate 112 0.5366 1.07E−09 0.2879glutamate*gondoate-20-1-n-9- 112 0.5823 1.65E−11 0.3390glutamate*isoleucine 112 0.5442 5.59E−10 0.2961 glutamate*leucine 1120.5311 1.70E−09 0.2820 glutamate*palmitate (16:0) 112 0.5134 7.08E−090.2636 glutamate*stearate (18:0) 112 0.5742 3.63E−11 0.3297glutamate*valine* 112 0.5511 3.04E−10 0.3037 glutamate*Metabolite-11228112 0.6659 1.11E−15 0.4435 glutamate*Metabolite-11230 112 0.59643.98E−12 0.3557 glutamate*Metabolite-11231 112 0.5182 4.85E−09 0.2686glutamate*Metabolite-11234 112 0.7245 0 0.5249glutamate*Metabolite-11235 112 0.5706 5.09E−11 0.3256glutamate*Metabolite-11242 112 0.6197 3.21E−13 0.3841glutamate*Metabolite-11249 112 0.6040 1.80E−12 0.3648glutamate*Metabolite-11252 112 0.6137 6.24E−13 0.3767glutamate*Metabolite-11258 112 0.5232 3.24E−09 0.2738glutamate*Metabolite-11387 112 0.5860 1.14E−11 0.3434glutamate*Metabolite-11432 112 0.6245 1.87E−13 0.3900glutamate*Metabolite-11434 112 0.6707 6.66E−16 0.4498glutamate*Metabolite-11435 112 0.5301 1.84E−09 0.2810glutamate*Metabolite-11628 112 0.5606 1.30E−10 0.3142glutamate*Metabolite-11887 112 0.5450 5.21E−10 0.2970glutamate*Metabolite-11897 112 0.6695 6.66E−16 0.4482glutamate*Metabolite-12037 112 0.5761 3.01E−11 0.3319glutamate*Metabolite-12061 112 0.7919 0 0.6271glutamate*Metabolite-12064 112 0.6742 4.44E−16 0.4545glutamate*Metabolite-3078 112 −0.5143 6.62E−09 0.2645glutamate*Metabolite-3100 112 0.6438 1.91E−14 0.4145glutamate*Metabolite-4055 112 0.7284 0 0.5305 glutamate*Metabolite-4986112 0.5229 3.34E−09 0.2734 glutamate*Metabolite-5847 112 0.6457 1.51E−140.4170 glutamate*Metabolite-6446 112 0.7149 0 0.5111glutamate*Metabolite-6488 112 0.6599 2.44E−15 0.4355glutamate*Metabolite-6627 112 0.5376 9.88E−10 0.2890glutamate*Metabolite-9033 112 0.8164 0 0.6665 glutamate*Metabolite-9043112 0.6741 4.44E−16 0.4544 glutamate*Metabolite-9299 112 0.5571 1.78E−100.3103 glutamate*Metabolite-9727 112 0.5597 1.41E−10 0.3132Metabolite-3078*1,5-anhydroglucitol-1,5 (AG) 112 0.5166 5.54E−09 0.2668Metabolite-3078*gamma-glutamylglutamine 112 0.5975 3.54E−12 0.3570Metabolite-4055*1,5-anhydroglucitol-1,5 (AG) 112 −0.5505 3.20E−10 0.3031pyruvate*Metabolite-4357 112 0.5054 1.32E−08 0.2554pyruvate*Metabolite-4360 112 0.7299 0 0.5328Metabolite-4769*gamma-glutamylglutamine 112 0.6458 1.49E−14 0.4171Metabolite-5847*1,5-anhydroglucitol-1,5 (AG) 112 −0.5083 1.05E−08 0.2584Metabolite-6446*1,5-anhydroglucitol-1,5 (AG) 112 −0.5887 8.71E−12 0.3465Metabolite-9033*1,5-anhydroglucitol-1,5 (AG) 112 −0.5674 6.87E−11 0.3220Metabolite-9033*Metabolite-11228 112 0.6119 7.64E−13 0.3744Metabolite-9033*Metabolite-11230 112 0.6313 8.50E−14 0.3986Metabolite-9033*Metabolite-11231 112 0.5342 1.31E−09 0.2854Metabolite-9033*Metabolite-11234 112 0.6833 0 0.4669Metabolite-9033*Metabolite-11235 112 0.5267 2.44E−09 0.2774Metabolite-9033*Metabolite-11242 112 0.5712 4.84E−11 0.3262Metabolite-9033*Metabolite-11249 112 0.5127 7.51E−09 0.2629Metabolite-9033*Metabolite-11252 112 0.5285 2.10E−09 0.2794Metabolite-9033*Metabolite-11387 112 0.5259 2.60E−09 0.2766Metabolite-9033*Metabolite-11432 112 0.6192 3.42E−13 0.3834Metabolite-9033*Metabolite-11434 112 0.6631 1.55E−15 0.4396Metabolite-9033*Metabolite-11435 112 0.5165 5.56E−09 0.2668Metabolite-9033*Metabolite-11628 112 0.5228 3.36E−09 0.2733Metabolite-9033*Metabolite-11897 112 0.6761 2.22E−16 0.4572Metabolite-9033*Metabolite-12061 112 0.7544 0 0.5691Metabolite-9033*Metabolite-12064 112 0.5456 4.93E−10 0.2977Metabolite-9033*Metabolite-9043 112 0.5697 5.53E−11 0.3246Metabolite-9033*Metabolite-9045 112 0.5596 1.42E−10 0.3131Metabolite-9033*Metabolite-9299 112 0.6908 0 0.4772

TABLE 9B Correlated Biomarkers in Study 2. p- Compounds N CorrelationR-square values HDL_Cholesterol*Adiponectin 397 0.511148 0.261272 <0.001Fat_Mass*BMI 402 0.843078 0.710781 <0.001 Weight*BMI 402 0.8046810.647512 <0.001 Waist*BMI 398 0.800452 0.640724 <0.001 Hip*BMI 3980.705318 0.497473 <0.001 Fat_Mass_pcnt*BMI 402 0.602829 0.363403 <0.001BMI*HOMA 388 0.590842 0.349094 <0.001 BMI*Fasting_Insulin 388 0.5897490.347804 <0.001 BMI*QUICKI 388 −0.580267 0.336710 <0.001 RD*BMI 402−0.551166 0.303784 <0.001 BMI*Fasting_C_Peptide 401 0.542661 0.294480<0.001 Fasting_C_Peptide*HOMA 388 0.829625 0.688277 <0.001Fasting_Insulin*Fasting_C_Peptide 388 0.828392 0.686233 <0.001Fasting_C_Peptide*QUICKI 388 −0.768811 0.591070 <0.001Fasting_Proinsulin*Fasting_C_Peptide 398 0.570761 0.325768 <0.001Fat_Mass*Fasting_C_Peptide 401 0.519632 0.270017 <0.001RD*Fasting_C_Peptide 401 −0.506727 0.256773 <0.001Waist*Fasting_C_Peptide 397 0.501492 0.251495 <0.001Fasting_Insulin*HOMA 388 0.979376 0.959178 <0.001 Fasting_Insulin*QUICKI388 −0.880137 0.774641 <0.001 Fasting_Insulin*Fasting_Proinsulin 3860.509757 0.259853 <0.001 Fat_Mass*Fasting_Insulin 388 0.576818 0.332719<0.001 Waist*Fasting_Insulin 384 0.502325 0.252330 <0.001Fasting_Proinsulin*HOMA 386 0.525130 0.275761 <0.001Fasting_FFA*palmitate (16:0) 393 0.552703 0.305480 <0.001Fasting_FFA*oleate (18:1(n-9)) 393 0.519978 0.270377 <0.001Fasting_FFA*linoleate (18:2(n-6)) 393 0.504094 0.254111 <0.001Fasting_FFA*Heptadecenate 393 0.503364 0.253375 <0.0012-aminobutyrate*2-hydroxybutyrate (AHB) 270 0.526705 0.277419 <0.001alpha linolenate (18:3(n-3))*Isobar-cis-9-cis-11-trans-11- 270 0.6344410.402516 <0.001 eicosenoate alpha linolenate (18:3(n-3))*linoleate(18:1(n-9)) (18:2(n-6)) 270 0.561647 0.315447 <0.001 alpha linolenate(18:3(n-3))*myristate (14:0) 270 0.656699 0.431254 <0.001 alphalinolenate (18:3(n-3))*myristoleate (18:1(n-9))*14-1-n-5- 270 0.5803750.336836 <0.001 alpha linolenate (18:3(n-3))*n-3-DPA-22-5-n-3- 2700.730453 0.533562 <0.001 alpha linolenate (18:3(n-3))*oleate (18:1(n-9))270 0.576371 0.332204 <0.001 alpha linolenate (18:3(n-3))*palmitate(16:0) 270 0.656120 0.430494 <0.001 alpha linolenate(18:3(n-3))*palmitoleate (16:1(n-7)) 270 0.631278 0.398512 <0.001 alphalinolenate (18:3(n-3))*stearate (18:0) 270 0.592125 0.350612 <0.001alpha linolenate (18:3(n-3))*Metabolite-11261 270 0.545276 0.297326<0.001 alpha linolenate (18:3(n-3))*Heptadecenate 270 0.645969 0.417276<0.001 alpha linolenate (18:3(n-3))*Metabolite-11521 270 0.5364720.287803 <0.001 5-oxoproline*gamma-glutamylleucine 270 0.634304 0.402341<0.001 aspartate*gamma-glutamylleucine 270 0.673200 0.453199 <0.001erythronate-*gamma-glutamylleucine 270 0.645586 0.416781 <0.001gamma-glutamylleucine*gamma-glutamylmethionine- 270 0.624245 0.389682<0.001 gamma-glutamylleucine*gammaglutamylphenylalanine 270 0.7973560.635776 <0.001 gamma-glutamylleucine*gamma-glutamylthreonine- 2700.590454 0.348635 <0.001 gamma-glutamylleucine*gamma-glutamyltyrosine270 0.709135 0.502873 <0.001 gamma-glutamylleucine*glutamine 270−0.589607 0.347636 <0.001 gamma-glutamylleucine*glycerate 270 0.5159680.266223 <0.001 gamma-glutamylleucine*Metabolite-10814 270 0.5616430.315442 <0.001 gamma-glutamylleucine*Metabolite-11505 270 0.5800410.336448 <0.001 gamma-glutamylleucine*Metabolite-11560 270 0.5449840.297008 <0.001 gamma-glutamylleucine*Metabolite-12055 270 0.8182610.669551 <0.001 gamma-glutamylleucine*Metabolite-3078 270 −0.5447290.296730 <0.001 gamma-glutamylleucine*Metabolite-3114 270 0.6102660.372424 <0.001 gamma-glutamylleucine*Glutamate 402 0.813405 0.661627<0.001 gamma-glutamylleucine*glutamylvaline 270 0.980569 0.961516 <0.001glucose*mannose 270 0.569026 0.323791 <0.001 glucose*galactonic acid 2700.612109 0.374677 <0.001 5-oxoproline*gluconate 270 0.519968 0.270367<0.001 5-oxoproline*Glutamate 270 0.598936 0.358724 <0.001aspartate*Glutamate 270 0.647610 0.419399 <0.001 erythronate-*Glutamate270 0.577551 0.333565 <0.001 gamma-glutamylleucine*Glutamate 2700.702612 0.493664 <0.001 gamma-glutamylphenylalanine*Glutamate 2700.683581 0.467283 <0.001 gamma-glutamylthreonine-*Glutamate 270 0.5476830.299956 <0.001 gamma-glutamyltyrosine*Glutamate 270 0.656418 0.430885<0.001 glutamine*Glutamate 270 −0.693796 0.481352 <0.001glycerate*Glutamate 270 0.514091 0.264289 <0.001Metabolite-10814*Glutamate 270 0.683648 0.467375 <0.001Metabolite-11505*Glutamate 270 0.565409 0.319687 <0.001Metabolite-11560*Glutamate 270 0.596566 0.355891 <0.001Metabolite-12055*Glutamate 270 0.710157 0.504322 <0.001Metabolite-3078*Glutamate 270 −0.573092 0.328434 <0.001Metabolite-3114*Glutamate 270 0.717209 0.514389 <0.001Glutamate*glutamylvaline 402 0.815543 0.665110 <0.0015-oxoproline*glutamylvaline 270 0.567314 0.321845 <0.001aspartate*glutamylvaline 270 0.650190 0.422746 <0.001erythronate-*glutamylvaline 270 0.648632 0.420723 <0.001gamma-glutamylmethionine-*glutamylvaline 270 0.682431 0.465712 <0.001gammaglutamylphenylalanine*glutamylvaline 270 0.748588 0.560384 <0.001gamma-glutamylthreonine-*glutamylvaline 270 0.613004 0.375774 <0.001gamma-glutamyltyrosine*glutamylvaline 270 0.669454 0.448169 <0.001glutamine*glutamylvaline 270 −0.586263 0.343704 <0.001glycerate*glutamylvaline 270 0.500861 0.250862 <0.001Metabolite-10814*glutamylvaline 270 0.544487 0.296466 <0.001Metabolite-11505*glutamylvaline 270 0.571094 0.326149 <0.001Metabolite-11560*glutamylvaline 270 0.520971 0.271411 <0.001Metabolite-12055*glutamylvaline 270 0.818203 0.669456 <0.001Metabolite-3078*glutamylvaline 270 −0.536866 0.288225 <0.001Metabolite-3114*glutamylvaline 270 0.588849 0.346744 <0.001Docosatetraenate*Heptadecenate 402 0.731992 0.535812 <0.001Fasting_FFA*Heptadecenate 393 0.503364 0.253375 <0.001Heptadecenate*palmitate (16:0) 402 0.902155 0.813884 <0.001Heptadecenate*margarate (17:0) 402 0.827249 0.684341 <0.001Heptadecenate*stearate (18:0) 402 0.719541 0.517740 <0.001Heptadecenate*alpha linolenate (18:3(n-3)) 402 0.605486 0.366614 <0.001Isobar-cis-9-cis-11-trans-11-eicosenoate*Heptadecenate 270 0.7170410.514147 <0.001 linoleate (18:2(n-6))*Heptadecenate 270 0.6964730.485075 <0.001 myristate (14:0)*Heptadecenate 270 0.815585 0.665178<0.001 myristoleate (18:1(n-9))*14-1-n-5-*Heptadecenate 270 0.7643730.584266 <0.001 n-3-DPA-22-5-n-3-*Heptadecenate 270 0.600981 0.361178<0.001 oleate (18:1(n-9))*Heptadecenate 270 0.826866 0.683707 <0.001palmitoleate (16:1(n-7))*Heptadecenate 270 0.891137 0.794126 <0.001Heptadecenate*Metabolite-11909 270 0.500849 0.250850 <0.001Linolyl.LPC*Oleoyl.LPC 270 0.503307 0.253318 <0.001 hypoxanthine*lactate270 0.521393 0.271850 <0.001 dihomo-alpha-alpha linolenate(18:3(n-3))-20-3-n-3-*linoleate 270 0.513066 0.263237 <0.001 (18:2(n-6))Isobar-cis-9-cis-11-trans-11-eicosenoate*linoleate (18:2(n-6)) 2700.614356 0.377433 <0.001 linoleate (18:2(n-6))*myristate (14:0) 2700.777196 0.604033 <0.001 linoleate (18:2(n-6))*oleate (18:1(n-9)) 2700.764875 0.585034 <0.001 linoleate (18:2(n-6))*palmitate (16:0) 2700.591405 0.349760 <0.001 linoleate (18:2(n-6))*palmitoleate (16:1(n-7))270 0.667721 0.445851 <0.001 linoleate (18:2(n-6))*stearate (18:0) 4020.688839 0.474500 <0.001 Docosatetraenate*linoleate (18:2(n-6)) 4020.718624 0.516421 <0.001 linoleate (18:2(n-6))*margarate (17:0) 4020.658122 0.433124 <0.001 Docosatetraenate*oleate (18:1(n-9)) 4020.764928 0.585115 <0.001 margarate (17:0)*oleate (18:1(n-9)) 2700.510486 0.260596 <0.001 3-hydroxybutyrate (BHBA)*oleate (18:1(n-9)) 2700.576371 0.332204 <0.001 alpha linolenate (18:3(n-3))*oleate (18:1(n-9))270 0.736518 0.542459 <0.001Isobar-cis-9-cis-11-trans-11-eicosenoate*oleate (18:1(n-9)) 270 0.7771960.604033 <0.001 linoleate (18:1(n-9)) (18:2(n-6))*oleate (18:1(n-9)) 2700.709041 0.502739 <0.001 margarate (17:0)*oleate (18:1(n-9)) 2700.668674 0.447124 <0.001 myristate (14:0)*oleate (18:1(n-9)) 2700.587740 0.345438 <0.001 myristoleate (18:1(n-9))*14-1-n-5-*oleate(18:1(n-9)) 270 0.907290 0.823175 <0.001 oleate (18:1(n-9))*palmitate(16:0) 270 0.766301 0.587217 <0.001 oleate (18:1(n-9))*palmitoleate(16:1(n-7)) 270 0.765960 0.586695 <0.001 oleate (18:1(n-9))*stearate(18:0) 402 0.748928 0.560893 <0.001 pyruvate*Metabolite-4357 2700.586698 0.344214 <0.001 asparagine*serine 270 0.638729 0.407974 <0.001ornithine*serine 270 0.656649 0.431187 <0.001 serine*Metabolite-4274 2700.578680 0.334870 <0.001 dihomo-alpha-alpha linolenate(18:3(n-3))-20-3-n-3-*palmitate 270 0.516782 0.267063 <0.001 (16:0)Isobar-cis-9-cis-11-trans-11-eicosenoate*palmitate (16:0) 270 0.7032640.494580 <0.001 margarate (17:0)*palmitate (16:0) 270 0.752390 0.566091<0.001 myristate (14:0)*palmitate (16:0) 270 0.807589 0.652199 <0.001myristoleate (18:1(n-9))*14-1-n-5-*palmitate (16:0) 270 0.6582360.433274 <0.001 n-3-DPA-22-5-n-3-*palmitate (16:0) 270 0.553025 0.305836<0.001 palmitate (16:0)*palmitoleate (16:1(n-7)) 270 0.784704 0.615761<0.001 palmitate (16:0)*stearate (18:0) 270 0.843751 0.711916 <0.001palmitate (16:0)*Heptadecenate 270 0.851782 0.725532 <0.001 palmitate(16:0)*Docosatetraenate 270 0.533851 0.284997 <0.001Metabolite-9033*Metabolite-10750 270 0.550669 0.303236 <0.001bradykinin, hydroxyproline form-*peptide-HWESASXXR 270 0.587635 0.345314<0.001

2I: Predicting and Monitoring Insulin Resistance:

The biomarker panel and algorithm will measure insulin resistance (IR)which is a root cause of type 2 diabetes. The results will be presentedas an “IR Score™” which represents the level of insulin resistance ofthe subject. IR Scores will range from Normal Glucose Tolerance (NGT)through increasing levels (Low, Medium, High) of Impaired GlucoseTolerance (IGT). The IR Score™ will allow the physician to place thepatient on the spectrum of glucose tolerance, from normal to high. Forexample, an IR Score™ of 25 will put the patient in the Low IGT categorywhile an IR Score™ of 80 will put the patient in the High IGT category.

By determining the IR Score on an annual or semi-annual basis,physicians can monitor a patient's progression toward diabetes. Forexample, an IR score of 25 was obtained at a first time point, an IRScore of 34 was obtained at a second time point, an

IR Score of 40 was obtained at a third time point, an IR Score of 40 wasobtained at a third time point, an IR Score of 55 was obtained at afourth time point, and an IR Score of 80 was obtained at a fourth timepoint indicating an increase in IR and progression of disease fromnormal to highly impaired glucose tolerance. Using the biomarkers andalgorithm of the instant invention for progression monitoring will guidethe physician's decision to implement preventative measures such asdietary restrictions, exercise, or early-stage drug treatment. Anexample of a report demonstrating the use of the IR Score to monitor IRstatus over time is shown in FIG. 5.

TABLE 10 IR Score IR Score 1 to 100 ≦25 NGT 26 to 50 Low IGT 51 to 75Medium IGT 76 to 100 High IGT >100 Type 2 Diabetes2J: Biomarkers that Correlate with Glucose Tolerance Tests

Another study will be carried out to test the biomarkers discovered inthe instant invention with a new cohort and to discover additionalbiochemical biomarkers that correlate with insulin sensitivity (1S) andinsulin resistance (IR) as measured by the hyperinsulinemic euglycemic(HI) clamp (Table 11). Using the following study design, baselinefasting EDTA-plasma samples collected from NGT, IGT, IFG IGT/IFG anddiabetic subjects (total=250) will be analyzed.

TABLE 11 Summary of Study Subjects Condition Number of Subjects NGT 50IGT 50 IFG 50 IGT/IFG 50 T2D 50 Abbreviations NGT: Normal GlucoseTolerant (OGTT, <140 mg/dL or <7.8 mmol/L) IGT: Impaired GlucoseTolerant (OGTT, 140-199 mg/dL or 7.8-11.0 mmol/L) IFG: Impaired FastingGlucose (Fasting plasma glucose, 100-125 mg/dL or 5.6-6.9 mmol/L)IGT/IFG: IGT and/or IFG T2D: Type II Diabetes (OGTT, ≧200 mg/dL or ≧11.1mmol/L)

Example 3 Biomarkers for Metabolic Syndrome Related Disorders 3A:Biomarkers of Metabolic Syndrome

Biomarkers were discovered by (1) analyzing plasma and serum samplesdrawn from different groups of subjects to determine the levels ofmetabolites in the samples and then (2) statistically analyzing theresults to determine those metabolites that were differentially presentin the two groups.

The samples used for the analysis were obtained from 19 Caucasian malesaged 18-39, average age of 25.6, that had been diagnosed with metabolicsyndrome and 19 healthy, age-matched, Caucasian males.

T-tests were used to determine differences in the mean levels ofmetabolites between the two populations (i.e., Metabolic syndrome vs.Healthy controls).

Biomarkers:

As listed below in Tables 12 and 13, biomarkers were discovered thatwere differentially present between samples from subjects with MetabolicSyndrome and Control (healthy) subjects.

Tables 12 and 13 include, for each listed biomarker, the p-value andq-value determined in the statistical analysis of the data concerningthe biomarkers and an indication of the mean level in metabolicsyndrome, the mean level in the control, and the percentage differencein the metabolic syndrome mean level as compared to the healthy meanlevel in plasma (Table 12) and serum (Table 13). The term “Isobar” asused in the tables indicates the compounds that could not bedistinguished from each other on the analytical platform used in theanalysis (i.e., the compounds in an isobar elute at nearly the same timeand have similar (and sometimes exactly the same) quant ions, and thuscannot be distinguished). Comp_ID refers to the compound identificationnumber used as a primary key for that compound in the in-house chemicaldatabase. Library indicates the chemical library that was used toidentify the compounds. The number 50 refer to the GC library and thenumber 61 refers to the LC library.

TABLE 12 Metabolite biomarkers of Metabolic Syndrome in plasma % ChangeMet. Mean_Metabolic Syn. vs COMP_ID COMPOUND LIB_ID p-value q-valueSyndrome Mean_Ctrl Control 22290 2-propylpentanoic acid 50 0.2189 0.32914.75 0.89 1557%  10715 Metabolite - 2395 61 0.3183 0.3868 6.94 0.83736%  10327 Metabolite - 2281 61 0.0788 0.2083 1.97 0.72 174%  10092Metabolite - 2250 61 0.3313 0.3919 1.84 0.74 149%  569 caffeine 610.0806 0.2083 1.92 0.78 146%  22054 Metabolite - 8792 50 <0.00014.00E−04 1.71 0.76 125%  12796 Metabolite - 3114 50 0.0559 0.1731 1.890.89 112%  10286 Metabolite - 2272 61 0.0922 0.2131 2.09 1.01 107% 12751 Metabolite - 3073 50 0.0055 0.0575 3.09 1.6 93% 10672 Metabolite -2390 61 0.0045 0.0575 1.85 0.97 91% 18369 gamma-glu-leu 61 0.2496 0.34732.82 1.49 89% 14715 Metabolite - 3653 61 0.5859 0.5302 2.39 1.28 87%11056 Metabolite - 2568 61 0.3525 0.3951 3.38 1.84 84% 57 glutamic acid50 0.0403 0.1575 2.6 1.46 78% 9130 Metabolite - 2139 61 0.0027 0.04731.62 0.94 72% 1638 arginine 61 0.0795 0.2083 1.53 0.9 70% 24233Metabolite - 9855 61 0.327 0.3896 1.59 0.95 67% 22130 DL-3-phenyllacticacid 61 0.1899 0.3052 1.82 1.1 65% 17492 Metabolite - 4906 61 0.17140.2843 1.62 0.98 65% 21630 Metabolite - 8402 50 0.0044 0.0575 1.48 0.964% 17557 Metabolite - 4929 61 0.0132 0.0946 1.25 0.77 62% 15253Metabolite - 3832 61 0.4313 0.4494 2.22 1.38 61% 20842 Metabolite - 776561 0.2648 0.3613 2.17 1.35 61% 14837 Metabolite - 3707 61 0.8263 0.6053.14 1.97 59% 3147 xanthine 61 0.0204 0.1174 1.54 0.98 57% 21127monopalmitin 50 0.0025 0.0473 1.5 0.96 56% 2734gamma-L-glutamyl-L-tyrosine 61 0.1081 0.2168 1.93 1.24 56% 6413Metabolite - 1342-possible- 61 0.2081 0.3221 1.82 1.17 56%phenylacetylglutamine- 2132 citrulline 50 0.4298 0.4494 0.34 0.22 55%20830 Metabolite - 7762 61 0.0195 0.1168 1.43 0.93 54% 15996 aspartate50 0.2785 0.3701 2.81 1.83 54% 18118 Metabolite - 5346 50 0.016 0.10081.52 0.99 54% 15113 Metabolite - 3783 61 0.0978 0.2144 0.85 0.56 52%7171 Metabolite - 1643 61 0.3158 0.3868 2.06 1.36 51% 19377 Metabolite -6272 50 0.0031 0.0497 1.16 0.77 51% 16337 Metabolite - 4167 61 0.03330.1436 1.41 0.94 50% 12756 Metabolite - 3077 50 4.00E−04 0.045 1.93 1.348% 17390 Metabolite - 4806 50 0.0276 0.1362 1.23 0.83 48% 21418Isobar-56-includes-DL- 61 0.1632 0.2797 1.78 1.21 47% pipecolicacid-1-amino-1- cyclopentanecarboxylic acid 1125 isoleucine 50 0.09940.2148 1.13 0.77 47% 6847 Metabolite - 1496 61 0.0238 0.1264 1.45 0.9946% 12658 Metabolite - 3026 50 0.0062 0.0575 1.66 1.15 44% 18392theobromine 61 0.4972 0.4818 1.41 0.98 44% 13775 Metabolite - 3370 610.001 0.0473 1.52 1.06 43% 7933 Metabolite - 1911 61 0.5673 0.5208 1.441.01 43% 22320 Metabolite - 8889 50 0.0244 0.1264 0.72 0.51 41% 27278Metabolite - 10510 50 0.0213 0.1195 1.48 1.05 41% 11178 Metabolite -2608 61 0.0065 0.0575 1.24 0.88 41% 12656 Metabolite - 3025 50 0.00250.0473 1.59 1.13 41% 18882 taurodeoxycholic acid 61 0.2208 0.3294 1.871.33 41% 27513 indole-3-acetic acid 61 0.0439 0.1617 1.36 0.97 40% 13214Metabolite - 3183-possible- 61 0.2025 0.3182 2.06 1.48 39%gamma-L-glutamyl-L- phenylalanine 1481 inositol-1-phosphate 50 0.04440.1617 1.72 1.24 39% 60 leucine 50 0.0726 0.2034 1.12 0.81 38% 12780Metabolite - 3098 50 0.0023 0.0473 1.67 1.21 38% 12774 Metabolite - 309450 0.0057 0.0575 1.19 0.87 37% 1561 alpha-tocopherol 50 0.0599 0.17741.31 0.96 36% 12647 Metabolite - 3019 50 0.0024 0.0473 1.5 1.1 36% 17068Metabolite - 4627 61 0.8349 0.6052 1.66 1.22 36% 12960 Metabolite - 313461 0.0558 0.1731 1.25 0.92 36% 9491 Metabolite - 2185 61 0.2076 0.32211.18 0.87 36% 9172 Metabolite - 2000 61 0.0197 0.1168 1.15 0.85 35% 1898proline 61 0.0309 0.1425 1.36 1.01 35% 1299 tyrosine 61 0.0027 0.04731.3 0.97 34% 18829 phenylalanine 61 0.0014 0.0473 1.51 1.13 34% 12767Metabolite - 3087 50 0.3192 0.3868 1.24 0.93 33% 9905 Metabolite - 223161 0.0482 0.1629 1.45 1.09 33% 19372 Metabolite - 6269 50 0.0255 0.12881.01 0.76 33% 19397 Metabolite - 6326 50 0.016 0.1008 1.38 1.04 33% 1649valine 50 0.1994 0.3156 1.1 0.83 33% 12222 Metabolite - 2374 50 0.00680.0575 1.37 1.04 32% 15140 L-kynurenine 61 0.0123 0.0912 1.33 1.01 32%5628 Metabolite - 1086 61 0.8915 0.6155 1.95 1.49 31% 5687 Metabolite -1110 61 0.6883 0.573 1.54 1.18 31% 20699 meso-erythritol 50 0.04660.1629 1.29 0.99 30% 15990 L-alpha- 61 0.2682 0.3634 1.94 1.49 30%glycerophosphorylcholine 27718 creatine 61 0.0922 0.2131 1.47 1.13 30%12609 Metabolite - 2986 50 0.0331 0.1436 1.82 1.4 30% 18476 glycocholicacid 61 0.1687 0.283 1.91 1.47 30% 18010 Metabolite - 5231 61 0.21640.329 1.52 1.17 30% 12876 Metabolite - 3125 61 0.0652 0.1905 1.22 0.9430% 19364 Metabolite - 6246 50 0.0105 0.0809 1.32 1.02 29% 10245Metabolite - 2269- 61 0.84 0.6052 1.5 1.16 29% 6266 Metabolite - 1286 610.092 0.2131 1.59 1.23 29% 15506 choline 61 0.1324 0.2451 1.71 1.33 29%12639 Metabolite - 3012 50 0.0024 0.0473 1.59 1.24 28% 16518Metabolite - 4276 50 0.1013 0.2164 1.14 0.89 28% 17512 Metabolite - 491261 0.5453 0.5139 2.99 2.34 28% 29817 Metabolite - 10683 50 0.0151 0.10081.57 1.23 28% 24076 Metabolite - 9726 50 0.0364 0.1465 1.34 1.06 26% 584mannose 50 0.1042 0.2168 1.39 1.1 26% 18524 6-hydroxydopamine 50 0.33520.3923 1.06 0.84 26% 1126 alanine 50 0.1098 0.2168 0.97 0.77 26% 10629Metabolite - 2386 61 0.5698 0.5208 1.2 0.96 25% 1301 lysine 50 0.34510.3929 1.01 0.81 25% 27256 Metabolite - 10500 50 0.0367 0.1465 1.07 0.8624% 9024 Metabolite - 2111 61 0.0925 0.2131 0.98 0.79 24% 10746Isobar-6-includes-valine- 61 0.1592 0.2751 1.47 1.19 24% betaine 12768Metabolite - 3088 50 0.1478 0.2642 1.85 1.5 23% 1572 glyceric acid 500.2335 0.3381 1.6 1.3 23% 12650 Metabolite - 3022 50 0.0772 0.2083 1.451.18 23% 22337 Metabolite - 8893 61 0.1269 0.2414 1.08 0.88 23% 10087Metabolite - 2249 61 0.3326 0.3919 1.36 1.11 23% 1670 urea 50 0.04450.1617 1.31 1.07 22% 527 lactate 50 0.2905 0.3718 1.54 1.26 22% 16496Metabolite - 4251 50 0.5501 0.5161 0.88 0.72 22% 8336 Metabolite - 200561 0.2177 0.329 1.29 1.06 22% 1303 malic acid 50 0.754 0.5875 1.07 0.8822% 15737 hydroxyacetic acid 50 0.0844 0.2083 1.08 0.89 21% 16819Metabolite - 4496 50 0.0589 0.177 1.2 0.99 21% 1358 octadecanoic acid 500.0073 0.0584 1.15 0.95 21% 17665 p-hydroxybenzaldehyde 61 0.0329 0.14361.84 1.52 21% 7081 Metabolite - 1609 61 0.3512 0.3951 1.05 0.87 21%10737 Isobar-1-includes-mannose- 61 0.0589 0.177 1.3 1.08 20%fructose-glucose-galactose- alpha-L-sorbopyranose-Inositol-D-allose-D--altrose- D-psicone-L--gulose-allo- inositol 13557Metabolite - 3323 61 0.5667 0.5208 1.26 1.05 20% 15122 glycerol 500.0822 0.2083 1.21 1.01 20% 16511 Metabolite - 4274 50 0.4771 0.47331.15 0.96 20% 1121 heptadecanoic acid 50 0.0531 0.1724 1.23 1.03 19%11053 Metabolite - 2567 61 0.5088 0.4907 3.1 2.6 19% 220261-methylguanidine 50 0.0457 0.1629 1.19 1 19% 25609 Metabolite - 1043950 0.3468 0.3929 1.63 1.37 19% 12035 nonanate 50 0.0892 0.2131 1.47 1.2419% 1110 arachidonic acid 50 0.1038 0.2168 1.11 0.94 18% 54 tryptophan61 0.0487 0.1629 1.28 1.09 17% 15278 Metabolite - 3843 61 0.4117 0.43781.15 0.98 17% 27570 Metabolite - 10569 61 0.0063 0.0575 1.09 0.93 17%30178 Metabolite - 10705 61 0.7541 0.5875 1.64 1.4 17% 63 cholesterol 500.0343 0.1446 1.17 1 17% 10551 Metabolite - 2347 61 0.6957 0.573 1.961.68 17% 21188 1-stearoyl-rac-glycerol 50 0.4701 0.4687 1.14 0.98 16%1365 tetradecanoic acid 50 0.071 0.2016 1.15 0.99 16% 5426 Metabolite -1004 61 0.3901 0.4235 1.08 0.93 16% 19368 Metabolite - 6267 50 0.23810.3381 1.59 1.37 16% 27273 Metabolite - 10506 50 0.1692 0.283 1.38 1.1916% 7029 Metabolite - 1597 61 0.0558 0.1731 1.77 1.53 16% 10156Metabolite - 2259 61 0.7281 0.5856 1.04 0.9 16% 10700 Metabolite - 239361 0.6334 0.5495 2.23 1.93 16% 13142 Metabolite - 3165 61 0.0829 0.20831.34 1.16 16% 25602 Metabolite - 10432 50 0.9349 0.6253 2.17 1.88 15%1431 p-hydroxyphenyllactic acid 50 0.2345 0.3381 1.23 1.07 15% 27271Metabolite - 10504 50 0.2474 0.3466 1.08 0.94 15% 6398 Metabolite - 133561 0.8947 0.6155 1.89 1.66 14% 1336 n-hexadecanoic acid 50 0.1174 0.22971.09 0.96 14% 27672 3-indoxyl-sulfate 61 0.4812 0.4752 1.3 1.15 13%22895 Metabolite - 9299 50 0.7239 0.5856 1.05 0.93 13% 12129beta-hydroxyisovaleric acid 50 0.3035 0.3814 1.4 1.24 13% 19282Metabolite - 6126 61 0.8029 0.6047 1.14 1.01 13% 21069 dioctyl-phthalate50 0.0953 0.2144 1.07 0.95 13% 17064 Metabolite - 4624 50 0.0925 0.21311.25 1.11 13% 21128 1-octadecanol 50 0.0486 0.1629 1.08 0.96 13% 18232Metabolite - 5403 50 0.1348 0.2473 1.19 1.06 12% 15529 Metabolite - 395161 0.1081 0.2168 1.29 1.15 12% 27675 4-nitrophenol 61 0.3162 0.3868 1.271.14 11% 9216 Metabolite - 2168 61 0.0835 0.2083 1.2 1.08 11% 10750Isobar-8-includes-anthranilic 61 0.2166 0.329 1.2 1.08 11%acid-salicylamide 7601 Metabolite - 1819 61 0.5864 0.5302 1.2 1.08 11%1604 uric acid 61 0.2964 0.3747 1.04 0.94 11% 513 creatinine 61 0.18850.3052 1.06 0.96 10% 1361 pentadecanoic acid 50 0.2362 0.3381 1.17 1.0610% 1642 decanoic acid 50 0.3114 0.3866 1.39 1.26 10% 18147 Metabolite -5367 50 0.0753 0.2083 1.11 1.01 10% 22803 Isobar-66-includes- 61 0.410.4378 1.69 1.54 10% glycochenodeoxycholic acid- glycodeoxycholic acid20267 Metabolite - 7187 61 0.9304 0.6243 2.16 1.97 10% 5531 Metabolite -1095 61 0.5242 0.5009 0.69 0.63 10% 19363 Metabolite - 6227 50 0.34170.3923 1.3 1.19  9% 1105 Linoleic acid 50 0.3425 0.3923 1.07 0.98  9%17228 Metabolite - 4727 61 0.7215 0.5856 1.56 1.43  9% 1643 fumaric acid50 0.9687 0.6344 1.46 1.34  9% 16782 Metabolite - 4470 61 0.9699 0.63441.12 1.03  9% 1302 methionine 61 0.2524 0.3489 1.27 1.17  9% 13545Metabolite - 3322 61 0.9964 0.6461 1.93 1.78  8% 12083 D-ribose 500.6963 0.573 1.31 1.21  8% 20950 Metabolite - 7846 50 0.4339 0.4499 1.321.22  8% 5765 Metabolite - 1142 61 0.6088 0.5395 1.21 1.12  8% 27719galactonic acid 50 0.6632 0.5631 1.08 1  8% 27409 oleamide 50 0.58820.5302 0.95 0.88  8% 1507 palmitoleic acid 50 0.7754 0.5955 1.25 1.16 8% 24077 Metabolite - 9727 50 0.5913 0.5308 1.28 1.19  8% 20489D-glucose 50 0.0349 0.1446 1.14 1.06  8% 6422 Metabolite - 1320 610.1095 0.2168 1.02 0.95  7% 19787 Metabolite - 6746 61 0.2617 0.35941.17 1.09  7% 5632 Metabolite - 1138 61 0.156 0.2741 1.03 0.96  7% 8098Metabolite - 1867 61 0.6653 0.5631 1.03 0.96  7% 30273 Metabolite -10736 50 0.6061 0.5394 1.18 1.1  7% 19934 inositol 50 0.6428 0.553 1.351.26  7% 15676 3-methyl-2-oxovaleric acid 61 0.5389 0.5102 1.21 1.13  7%18349 DL-indole-3-lactic acid 61 0.8534 0.6083 1.22 1.14  7% 15765ethylmalonic acid 61 0.7431 0.5875 0.99 0.93  6% 30282 Metabolite -10744 50 0.4613 0.4621 1.17 1.1  6% 16138 Metabolite - 4080 50 0.81360.6047 2.4 2.27  6% 10544 Metabolite - 2329 61 0.9173 0.6207 1.11 1.05 6% 15500 carnitine 61 0.6276 0.5468 0.94 0.89  6% 12645 Metabolite -3017 50 0.6906 0.573 1.32 1.25  6% 16665 Metabolite - 4364 50 0.74070.5875 1.16 1.1  5% 17648 Metabolite - 5007 61 0.886 0.6154 2.2 2.09  5%15365 sn-Glycerol-3-phosphate 50 0.8826 0.6154 1.8 1.71  5% 10499Metabolite - 2073 61 0.5624 0.5208 1.02 0.97  5% 12638 Metabolite - 301150 0.9472 0.6287 1.31 1.25  5% 12663 Metabolite - 3030 50 0.7315 0.58561.55 1.48  5% 10065 Metabolite - 2244 61 0.6638 0.5631 0.95 0.91  4%1645 n-dodecanoate 50 0.451 0.4584 1.24 1.19  4% 6305 Metabolite - 125461 0.7656 0.5901 0.82 0.79  4% 18665 Metabolite - 5728 61 0.6202 0.54511.14 1.1  4% 13065 Metabolite - 3146 61 0.6037 0.5394 1.21 1.17  3% 7127Metabolite - 1616 61 0.9018 0.6171 0.92 0.89  3% 12673 Metabolite - 304050 0.638 0.5512 1.29 1.25  3% 59 histidine 50 0.8614 0.6083 1 0.97  3%27275 Metabolite - 10507 50 0.8966 0.6155 1.36 1.32  3% 12626Metabolite - 3003 50 0.7594 0.5875 1.1 1.07  3% 17627 Metabolite - 498650 0.8711 0.6123 1.23 1.2  2% 13589 Metabolite - 3327 61 0.919 0.62071.51 1.48  2% 12894 Metabolite - 2456 61 0.9618 0.6331 1.05 1.03  2%1648 serine 50 0.8095 0.6047 1.14 1.12  2% 20248 Metabolite - 7177 610.7567 0.5875 1.23 1.21  2% 25607 Metabolite - 10437 50 0.9076 0.61821.34 1.32  2% 1564 citric acid 50 0.4876 0.4779 0.04 0.04  0% 12726Metabolite - 3058 50 0.693 0.573 1.15 1.15  0% 12593 Metabolite - 297350 0.7576 0.5875 0.43 0.43  0% 14988 Metabolite - 3756 61 0.9471 0.62871.1 1.1  0% 10147 Metabolite - 2036 61 0.9971 0.6461 1.26 1.27 −1% 16829Metabolite - 4503 50 0.8459 0.6069 1.19 1.2 −1% 27411 Metabolite - 1054761 0.9605 0.6331 1.04 1.05 −1% 1410 1-Hexadecanol 50 0.8407 0.6052 0.970.98 −1% 10655 Metabolite - 2388 61 0.7806 0.5973 1.1 1.12 −2% 17327Metabolite - 4767 50 0.8114 0.6047 1.09 1.11 −2% 12666 Metabolite -3033-possible- 50 0.6852 0.573 1.23 1.26 −2% threonine-deriv- 1366trans-4-hydroxyproline 50 0.9284 0.6243 1.09 1.12 −3% 210473-methyl-2-oxobutyric- 61 0.6204 0.5451 0.99 1.02 −3% 10825 Metabolite -2546 61 0.7995 0.6047 0.98 1.01 −3% 16070 Metabolite - 4019 50 0.71730.5856 1.19 1.23 −3% 22132 DL-alpha-hydroxyisocaproic 61 0.8586 0.60831.17 1.21 −3% acid 17786 aldosterone 61 0.8368 0.6052 1.1 1.14 −4% 30265Metabolite - 10732 50 0.6895 0.573 2.46 2.55 −4% 19097 Metabolite - 596961 0.9094 0.6182 0.83 0.87 −5% 22145 acetyl-L-carnitine 61 0.7304 0.58561.14 1.2 −5% 1494 5-oxoproline 50 0.8287 0.605 1.33 1.4 −5% 22309Metabolite - 8887 61 0.8136 0.6047 1.89 1.99 −5% 6571 Metabolite - 139761 0.7119 0.5834 0.93 0.98 −5% 16509 Metabolite - 4273 50 0.4072 0.43781.3 1.37 −5% 19623 Metabolite - 6671 50 0.563 0.5208 0.33 0.35 −6% 6517Metabolite - 1338 61 0.4487 0.4583 0.98 1.04 −6% 12162 Metabolite - 233950 0.427 0.4494 0.62 0.66 −6% 5733 Metabolite - 1127 61 0.4107 0.43781.2 1.28 −6% 27272 Metabolite - 10505 50 0.2265 0.3355 1.38 1.48 −7% 58glycine 50 0.4963 0.4818 0.96 1.03 −7% 12777 Metabolite - 3097 50 0.52840.5025 3.1 3.33 −7% 17568 Metabolite - 4931 61 0.7564 0.5875 1.05 1.13−7% 13038 Metabolite - 3143 61 0.2742 0.3667 1.29 1.39 −7% 20299Metabolite - 7266 50 0.341 0.3923 0.86 0.93 −8% 12720 Metabolite - 305661 0.5179 0.4971 1.1 1.19 −8% 12782 Metabolite - 3100 50 0.7853 0.59861.94 2.1 −8% 22609 Metabolite - 9047 50 0.8612 0.6083 1.45 1.57 −8% 2761thyroxine 61 0.3227 0.3868 1.2 1.3 −8% 1284 threonine 50 0.4887 0.47790.99 1.08 −8% 22548 Metabolite - 9026 50 0.8228 0.605 1.01 1.11 −9% 6851Metabolite - 1497 61 0.7431 0.5875 0.69 0.76 −9% 7644 Metabolite - 1831-61 0.1522 0.2697 1.05 1.16 −9% 22880 Metabolite - 9286 50 0.3811 0.41590.38 0.42 −10%  12533 Metabolite - 2915 50 0.0688 0.1983 0.9 1 −10% 25402 Metabolite - 10360 50 0.4214 0.4458 0.7 0.78 −10%  20676 maleicacid 61 0.3809 0.4159 0.62 0.7 −11%  6362 Metabolite - 1323-possible-p-61 0.8874 0.6154 1.22 1.38 −12%  cresol-sulfate 22133DL-hexanoyl-carnitine 61 0.289 0.3718 0.53 0.6 −12%  17304 Metabolite -4759 61 0.2874 0.3718 0.98 1.11 −12%  16468 Metabolite - 4236 61 0.11870.23 0.87 0.99 −12%  5618 Metabolite - 1085 61 0.3652 0.4028 0.97 1.11−13%  30555 Metabolite - 10781 61 0.8625 0.6083 1.28 1.47 −13%  6373Metabolite - 1304 61 0.2945 0.3747 1.18 1.36 −13%  22175l-aspartyl-l-phenylalanine 61 0.8236 0.605 1.03 1.19 −13%  16071Metabolite - 4020 50 0.1303 0.2435 0.93 1.08 −14%  19402 Metabolite -6346 50 0.0066 0.0575 0.97 1.13 −14%  22600 Metabolite - 9043 50 0.34070.3923 1.09 1.27 −14%  17330 Metabolite - 4769 50 0.2308 0.3381 0.921.08 −15%  2342 serotonin 61 0.4557 0.4609 0.89 1.05 −15%  3127hypoxanthine 61 0.2372 0.3381 1.54 1.82 −15%  15128 DL-homocysteine 610.0794 0.2083 0.81 0.96 −16%  16512 Metabolite - 4275 50 0.2404 0.3391.04 1.27 −18%  17494 Metabolite - 4907 61 0.9491 0.6287 1.53 1.87 −18% 19370 Metabolite - 6268 50 0.2874 0.3718 0.91 1.12 −19%  14672Metabolite - 3615 61 0.984 0.6416 0.88 1.09 −19%  577 fructose 50 0.27160.3657 0.96 1.19 −19%  11499 Metabolite - 2753 61 0.459 0.4619 0.69 0.86−20%  6374 Metabolite - 1327 61 0.3602 0.4016 0.94 1.18 −20%  220533-hydroxydecanoic acid 61 0.3226 0.3868 1.04 1.31 −21%  27738 threonicacid 50 0.1963 0.3131 1.02 1.29 −21%  542 3-hydroxybutanoic acid 500.3099 0.3866 1.24 1.57 −21%  12757 Metabolite - 3078 50 0.1683 0.283 11.27 −21%  12781 Metabolite - 3099 50 0.2872 0.3718 2.88 3.67 −22% 24074 Metabolite - 9706 50 0.2808 0.3708 0.85 1.1 −23%  53 glutamine 500.0982 0.2144 0.71 0.92 −23%  14239 Metabolite - 3474 61 0.1845 0.30121.02 1.33 −23%  12625 Metabolite - 3002 50 0.0973 0.2144 0.68 0.9 −24% 19110 Metabolite - 5978 50 0.1078 0.2168 0.64 0.85 −25%  17540Metabolite - 4926 61 0.4366 0.4504 2.34 3.13 −25%  22570 Metabolite -9033 50 0.1374 0.2477 0.25 0.34 −26%  10961 Metabolite - 2561 61 0.57010.5208 1.31 1.81 −28%  10604 Metabolite - 2370 61 0.1251 0.2402 0.680.95 −28%  5657 Metabolite - 1092 61 0.6524 0.5589 3.89 5.46 −29%  18705Metabolite - 5768 61 0.158 0.2751 1.03 1.48 −30%  16044 Metabolite -4005 50 0.0144 0.0992 0.55 0.81 −32%  17091 Metabolite - 4641 61 0.17420.2866 0.76 1.12 −32%  10066 Metabolite - 2029 61 0.4461 0.4579 1.191.77 −33%  22159 dehydroisoandrosterone-3- 61 0.0303 0.1425 0.93 1.4−34%  sulfate 22649 Metabolite - 9108 50 0.0532 0.1724 0.66 1 −34% 17306 Metabolite - 4760 61 0.098 0.2144 0.82 1.25 −34%  9165Metabolite - 2150 61 0.1062 0.2168 0.76 1.16 −34%  6239 Metabolite -1264 61 0.1367 0.2477 2.92 4.84 −40%  10781 Metabolite - 2469 61 0.02310.126 0.89 1.51 −41%  10304 Metabolite - 2276 61 0.8813 0.6154 1.28 2.19−42%  5280 biliverdin 61 0.0289 0.1395 1.19 2.14 −44%  18871Metabolite - 5848 61 0.0069 0.0575 1.03 1.88 −45%  18702 Metabolite -5767 61 0.0017 0.0473 0.76 1.45 −48%  12478 Metabolite - 2898 61 0.12850.2421 0.86 1.71 −50%  27710 N-acetylglycine 50 0.0049 0.0575 0.78 1.64−52%  17495 Metabolite - 4908 61 0.044 0.1617 0.94 1.99 −53%  10177Metabolite - 2039 61 0.0067 0.0575 0.83 1.86 −55%  12306 Metabolite -2869 61 0.627 0.5468 1.18 2.72 −57% 

TABLE 13 Metabolite biomarkers of Metabolic Syndrome in serum. % ChangeMet. Mean_Metabolic Syn. vs COMP_ID COMPOUND LIB_ID p-value q-valueSyndrome Mean_Control Control 19402 Metabolite - 6346 50 0 9.00E−04 0.881.07 −18%  22054 Metabolite - 8792 50 0 9.00E−04 1.29 0.56 130%  12663Metabolite - 3030 50 4.00E−04 0.0304 0.61 0.83 −27%  27710N-acetylglycine 50 6.00E−04 0.0335 0.56 1.28 −56%  18829 phenylalanine61 0.0015 0.0629 0.95 0.71 34% 13257 Metabolite - 3218 61 0.0019 0.06750.94 0.57 65% 9172 Metabolite - 2000 61 0.0023 0.0675 1.24 0.86 44%17390 Metabolite - 4806 50 0.0032 0.0827 1.2 0.69 74% 20830 Metabolite -7762 61 0.005 0.1061 1.21 0.69 75% 10672 Metabolite - 2390 61 0.00510.1061 1.05 0.63 67% 13142 Metabolite - 3165 61 0.0066 0.1177 0.99 0.7827% 18147 Metabolite - 5367 50 0.0068 0.1177 1.05 0.9 17% 19110Metabolite - 5978 50 0.0075 0.1206 1.08 1.66 −35%  16337 Metabolite -4167 61 0.0087 0.1269 1.33 0.8 66% 27570 Metabolite - 10569 61 0.00950.1269 1.15 0.94 22% 6422 Metabolite - 1320 61 0.0098 0.1269 1.07 0.9710% 21630 Metabolite - 8402 50 0.0104 0.1269 1.17 0.76 54% 1299 tyrosine61 0.0113 0.1304 1.26 0.87 45% 9491 Metabolite - 2185 61 0.0119 0.13041.14 0.77 48% 18702 Metabolite - 5767 61 0.013 0.132 0.75 1.31 −43% 13775 Metabolite - 3370 61 0.0138 0.132 0.93 0.68 37% 10177 Metabolite -2039 61 0.014 0.132 0.82 1.59 −48%  7081 Metabolite - 1609 61 0.01710.1442 1.02 0.84 21% 18871 Metabolite - 5848 61 0.0171 0.1442 0.86 1.32−35%  12658 Metabolite - 3026 50 0.0178 0.1442 0.98 0.79 24% 12647Metabolite - 3019 50 0.0191 0.1442 0.92 0.79 16% 12656 Metabolite - 302550 0.0193 0.1442 0.88 0.75 17% 18118 Metabolite - 5346 50 0.0194 0.14421.16 0.86 35% 17786 aldosterone 61 0.0209 0.1498 1.14 0.91 25% 27273Metabolite - 10506 50 0.0239 0.1655 0.77 0.91 −15%  17665 p- 61 0.02650.1775 0.7 0.53 32% hydroxybenzaldehyde 6374 Metabolite - 1327 61 0.02770.1781 1.23 1.92 −36%  7029 Metabolite - 1597 61 0.0305 0.1781 0.67 0.534% 21188 1-stearoyl-rac- 50 0.0308 0.1781 1.16 0.76 53% glycerol 16044Metabolite - 4005 50 0.0317 0.1781 1.01 1.55 −35%  5727 Metabolite -1126 61 0.0341 0.1781 1.19 0.96 24% 10737 Isobar-1-includes- 61 0.03430.1781 0.93 0.75 24% mannose-fructose- glucose-galactose- alpha-L-sorbopyranose- Inositol-D-allose-D-- altrose-D-psicone-L--gulose-allo-inositol 1303 malic acid 50 0.0353 0.1781 1.05 1.23 −15%  57glutamic acid 50 0.0361 0.1781 1.86 1 86% 14491 Metabolite - 3530 610.0362 0.1781 0.66 1.02 −35%  12478 Metabolite - 2898 61 0.0364 0.17810.64 1.64 −61%  6266 Metabolite - 1286 61 0.0369 0.1781 1.01 0.79 28%1638 arginine 61 0.0374 0.1781 1.49 0.81 84% 63 cholesterol 50 0.03780.1781 1.11 0.93 19% 15529 Metabolite - 3951 61 0.0386 0.1781 0.98 0.8417% 15278 Metabolite - 3843 61 0.0403 0.181 1.17 0.8 46% 27275Metabolite - 10507 50 0.0417 0.181 0.6 0.78 −23%  22159dehydroisoandrosterone- 61 0.0422 0.181 0.8 1.11 −28%  3-sulfate 19397Metabolite - 6326 50 0.0427 0.181 1.12 0.9 24% 12609 Metabolite - 298650 0.044 0.1812 0.88 0.7 26% 10087 Metabolite - 2249 61 0.0468 0.18121.21 0.84 44% 19377 Metabolite - 6272 50 0.0472 0.1812 1.24 0.95 31%1604 uric acid 61 0.0485 0.1812 1.12 0.96 17% 54 tryptophan 61 0.05010.1812 0.98 0.82 20% 15140 L-kynurenine 61 0.0507 0.1812 1.11 0.85 31%12666 Metabolite - 3033- 50 0.051 0.1812 0.82 0.95 −14% possible-threonine- deriv- 21127 monopalmitin 50 0.0517 0.1812 1.31 0.8260% 10629 Metabolite - 2386 61 0.0529 0.1812 1.08 0.82 32% 1125isoleucine 50 0.0533 0.1812 1.57 1.05 50% 12035 nonanate 50 0.05420.1812 0.75 0.58 29% 60 leucine 50 0.0543 0.1812 1.36 1 36% 12751Metabolite - 3073 50 0.0549 0.1812 0.74 0.64 16% 12781 Metabolite - 309950 0.0549 0.1812 0.52 0.75 −31%  12774 Metabolite - 3094 50 0.05660.1828 1.11 0.89 25% 24076 Metabolite - 9726 50 0.059 0.1828 1.09 0.921% 6571 Metabolite - 1397 61 0.0593 0.1828 1.18 0.94 26% 10499Metabolite - 2073 61 0.0594 0.1828 1.22 0.96 27% 27278 Metabolite -10510 50 0.0598 0.1828 1.05 0.82 28% 513 creatinine 61 0.0623 0.18751.15 0.93 24% 18665 Metabolite - 5728 61 0.0632 0.1878 0.97 0.85 14%18369 gamma-glu-leu 61 0.0679 0.1978 1.96 1.02 92% 19370 Metabolite -6268 50 0.0685 0.1978 0.79 0.97 −19%  20699 meso-erythritol 50 0.06970.1986 1.15 0.89 29% 9130 Metabolite - 2139 61 0.0709 0.1991 1.31 0.9242% 18392 theobromine 61 0.0727 0.2015 1.24 0.69 80% 1649 valine 500.074 0.2015 1.5 1.08 39% 18882 taurodeoxycholic acid 61 0.0747 0.20152.13 1.13 88% 27718 creatine 61 0.0798 0.2098 1.28 0.94 36% 27513indole-3-acetic acid 61 0.0815 0.2098 1.17 0.84 39% 9216 Metabolite -2168 61 0.0832 0.2098 1.02 0.84 21% 9905 Metabolite - 2231 61 0.08330.2098 1.08 0.86 26% 19414 Metabolite - 6350 50 0.0838 0.2098 1.33 1.0823% 20092 Metabolite - 7050 61 0.0844 0.2098 0.59 0.84 −30%  6435Metabolite - 1348 61 0.0848 0.2098 2.07 0.99 109%  1648 serine 50 0.090.22 0.91 1.09 −17%  13214 Metabolite - 3183- 61 0.0918 0.2214 1.4 0.9744% possible-gamma-L- glutamyl-L- phenylalanine 22649 Metabolite - 910850 0.0927 0.2214 0.93 1.17 −21%  30689 Metabolite - 10790 61 0.09390.2217 0.97 1.07 −9% 22548 Metabolite - 9026 50 0.0989 0.2309 2.37 0.98142%  1358 octadecanoic acid 50 0.1003 0.2317 1.04 0.89 17% 16512Metabolite - 4275 50 0.1067 0.2438 0.71 0.93 −24%  21069dioctyl-phthalate 50 0.1079 0.2438 1.34 0.88 52% 12960 Metabolite - 313461 0.1094 0.2446 1.27 1.05 21% 22609 Metabolite - 9047 50 0.1248 0.2720.89 0.42 112%  1898 proline 61 0.1257 0.272 1.12 0.88 27% 1642 decanoicacid 50 0.1266 0.272 0.85 0.74 15% 12726 Metabolite - 3058 50 0.12690.272 0.81 0.97 −16%  11974 Metabolite - 2827 61 0.1311 0.2779 1.03 1.24−17%  20842 Metabolite - 7765 61 0.1349 0.2833 1.61 0.97 66% 17304Metabolite - 4759 61 0.1386 0.2872 1.47 0.92 60% 15737 hydroxyaceticacid 50 0.1396 0.2872 2.37 0.81 193%  18010 Metabolite - 5231 61 0.14330.2919 1.16 0.83 40% 59 histidine 50 0.1489 0.2956 0.87 1.03 −16%  16665Metabolite - 4364 50 0.1496 0.2956 0.78 0.93 −16%  12757 Metabolite -3078 50 0.15 0.2956 0.67 0.89 −25%  1640 ascorbic acid 50 0.1509 0.29561.02 1.65 −38%  21418 Isobar-56-includes- 61 0.1522 0.2956 1.17 0.74 58%DL-pipecolic acid-1- amino-1- cyclopentanecarboxylic acid 10327Metabolite - 2281 61 0.1573 0.3028 1.44 0.85 69% 10700 Metabolite - 239361 0.1613 0.3032 1 0.8 25% 10961 Metabolite - 2561 61 0.1674 0.3032 0.910.72 26% 2734 gamma-L-glutamyl- 61 0.1684 0.3032 1.26 0.85 48%L-tyrosine 27409 oleamide 50 0.169 0.3032 2.13 1.09 95% 6847Metabolite - 1496 61 0.169 0.3032 1.27 1 27% 18705 Metabolite - 5768 610.1693 0.3032 0.68 0.97 −30%  27742 aconitate 61 0.1696 0.3032 1.92 1.0681% 19368 Metabolite - 6267 50 0.1697 0.3032 0.71 0.8 −11%  30265Metabolite - 10732 50 0.1716 0.3032 0.65 0.85 −24%  5423-hydroxybutanoic 50 0.1728 0.3032 0.91 1.24 −27%  acid 211281-octadecanol 50 0.174 0.3032 1.93 0.91 112%  1126 alanine 50 0.1750.3032 1.28 1.11 15% 9165 Metabolite - 2150 61 0.1811 0.3111 0.7 0.95−26%  8098 Metabolite - 1867 61 0.1854 0.3159 1.15 0.93 24% 20299Metabolite - 7266 50 0.1953 0.3285 0.92 1.04 −12%  12129 beta- 50 0.1960.3285 0.75 0.67 12% hydroxyisovaleric acid 12626 Metabolite - 3003 500.1999 0.3315 0.94 1.04 −10%  18476 glycocholic acid 61 0.201 0.33151.75 1.57 11% 12222 Metabolite - 2374 50 0.2083 0.3378 1.04 0.91 14%1361 pentadecanoic acid 50 0.2083 0.3378 2.3 0.96 140%  53 glutamine 500.2096 0.3378 1.02 1.23 −17%  22026 1-methylguanidine 50 0.2146 0.34081.01 0.92 10% 7127 Metabolite - 1616 61 0.2149 0.3408 1.02 1.28 −20% 10746 Isobar-6-includes- 61 0.217 0.3408 0.88 0.49 80% valine-betaine5426 Metabolite - 1004 61 0.2181 0.3408 1.97 1.02 93% 1302 methionine 610.2235 0.3428 0.91 0.83 10% 1121 heptadecanoic acid 50 0.2248 0.3428 1.50.94 60% 6373 Metabolite - 1304 61 0.2252 0.3428 0.77 1.32 −42%  25602Metabolite - 10432 50 0.226 0.3428 0.64 0.8 −20%  22145acetyl-L-carnitine 61 0.2282 0.343 1.07 1.24 −14%  18232 Metabolite -5403 50 0.2307 0.343 1.02 0.93 10% 1365 tetradecanoic acid 50 0.2310.343 1.31 0.86 52% 10750 Isobar-8-includes- 61 0.2353 0.346 0.93 0.8115% anthranilic acid- salicylamide 24233 Metabolite - 9855 61 0.2380.346 1.27 0.75 69% 6305 Metabolite - 1254 61 0.2381 0.346 2.42 1.05130%  30555 Metabolite - 10781 61 0.2409 0.3476 0.45 0.67 −33%  569caffeine 61 0.2479 0.3553 1.57 0.79 99% 12162 Metabolite - 2339 500.2496 0.3554 1.41 1.48 −5% 16829 Metabolite - 4503 50 0.2514 0.35540.83 0.96 −14%  15113 Metabolite - 3783 61 0.2569 0.3592 1.97 1.34 47%13211 Metabolite - 3182 61 0.2592 0.3592 1.63 1.05 55% 8336 Metabolite -2005 61 0.261 0.3592 1.07 0.89 20% 17330 Metabolite - 4769 50 0.2610.3592 0.79 0.91 −13%  12645 Metabolite - 3017 50 0.2635 0.3603 0.810.91 −11%  21047 3-methyl-2- 61 0.2676 0.3635 1 0.86 16% oxobutyric-5733 Metabolite - 1127 61 0.2766 0.3705 0.84 0.92 −9% 1670 urea 500.2793 0.3705 0.99 0.89 11% 1572 glyceric acid 50 0.2799 0.3705 0.9 0.6930% 1507 palmitoleic acid 50 0.2804 0.3705 0.86 1.07 −20%  1110arachidonic acid 50 0.2843 0.3716 1.03 0.91 13% 1561 alpha-tocopherol 500.2874 0.3733 1.07 0.8 34% 19364 Metabolite - 6246 50 0.2912 0.3759 0.910.79 15% 5765 Metabolite - 1142 61 0.2932 0.3761 0.99 0.86 15% 6405Metabolite - 1338 61 0.3015 0.3844 2.63 0.96 174%  16071 Metabolite -4020 50 0.3073 0.3884 0.92 1 −8% 16496 Metabolite - 4251 50 0.30850.3884 1.54 1.24 24% 12533 Metabolite - 2915 50 0.3102 0.3884 1.1 1.01 9% 10286 Metabolite - 2272 61 0.316 0.3933 1.45 1 45% 8469 Metabolite -2036- 61 0.3184 0.3939 0.88 0.55 60% possible-Heme 10065 Metabolite -2244 61 0.325 0.3991 1.02 0.95  7% 19623 Metabolite - 6671 50 0.32650.3991 2.43 2.77 −12%  27411 Metabolite - 10547 61 0.3324 0.4023 0.520.68 −24%  12777 Metabolite - 3097 50 0.3329 0.4023 0.53 0.7 −24%  18394theophylline 61 0.3355 0.4031 0.97 0.71 37% 2761 thyroxine 61 0.34180.4082 0.86 0.93 −8% 18254 paraxanthine 61 0.3437 0.4082 0.97 0.58 67%7933 Metabolite - 1911 61 0.3458 0.4083 1.13 0.77 47% 15996 aspartate 500.3567 0.417 0.63 0.49 29% 7601 Metabolite - 1819 61 0.3571 0.417 0.770.72  7% 1336 n-hexadecanoic acid 50 0.3594 0.4174 1.07 0.92 16% 10147Metabolite - 2036 61 0.3626 0.4186 0.9 0.99 −9% 5618 Metabolite - 108561 0.3702 0.4251 1.35 1.06 27% 15500 carnitine 61 0.377 0.429 1.07 0.9710% 6413 Metabolite - 1342- 61 0.3778 0.429 1.1 0.94 17% possible-phenylacetylglutamine- 27738 threonic acid 50 0.3822 0.4317 0.89 0.96−7% 22132 DL-alpha- 61 0.3947 0.4428 1 0.88 14% hydroxyisocaproic acid17306 Metabolite - 4760 61 0.3968 0.4428 0.74 1.09 −32%  20267Metabolite - 7187 61 0.399 0.4428 2.07 1.37 51% 14368 Metabolite - 348961 0.4005 0.4428 0.84 0.94 −11%  10825 Metabolite - 2546 61 0.405 0.44541.13 1.02 11% 6546 Metabolite - 1391 61 0.4074 0.4456 1.12 1.02 10%27675 4-nitrophenol 61 0.4103 0.4465 0.96 0.88  9% 17064 Metabolite -4624 50 0.4189 0.4534 0.88 0.92 −4% 20489 D-glucose 50 0.4214 0.45380.93 0.95 −2% 20676 maleic acid 61 0.4265 0.4553 1.18 1.33 −11%  13557Metabolite - 3323 61 0.4306 0.4553 1.3 1.02 27% 10551 Metabolite - 234761 0.4307 0.4553 1.97 1.41 40% 15990 L-alpha- 61 0.4316 0.4553 0.71 0.5822% glycerophosphorylcholine 6517 Metabolite - 1338 61 0.4377 0.45910.86 0.8  8% 19097 Metabolite - 5969 61 0.4396 0.4591 0.98 1.08 −9%22803 Isobar-66-includes- 61 0.442 0.4593 1.63 1.38 18%glycochenodeoxycholic acid- glycodeoxycholic acid 1410 1-Hexadecanol 500.4484 0.4635 2.68 1.01 165%  15765 ethylmalonic acid 61 0.4505 0.46350.95 0.81 17% 5280 biliverdin 61 0.4559 0.4663 0.86 1.01 −15%  1284threonine 50 0.4577 0.4663 1.14 1.24 −8% 30273 Metabolite - 10736 500.4611 0.4675 0.89 1 −11%  16518 Metabolite - 4276 50 0.4687 0.4717 0.860.72 19% 12756 Metabolite - 3077 50 0.4698 0.4717 0.73 0.68  7% 12639Metabolite - 3012 50 0.4752 0.4748 0.79 0.73  8% 12650 Metabolite - 302250 0.4937 0.4894 0.84 0.77  9% 10544 Metabolite - 2329 61 0.4945 0.48941.05 1.4 −25%  22337 Metabolite - 8893 61 0.5005 0.493 1.07 1.14 −6%18524 6-hydroxydopamine 50 0.5075 0.4953 1.13 1.2 −6% 13065 Metabolite -3146 61 0.5076 0.4953 1.01 0.97  4% 15506 choline 61 0.5148 0.5 1.020.75 36% 14988 Metabolite - 3756 61 0.5198 0.5007 0.68 0.74 −8% 12894Metabolite - 2456 61 0.5228 0.5007 0.9 0.94 −4% 22880 Metabolite - 928650 0.5255 0.5007 1.33 1.25  6% 7171 Metabolite - 1643 61 0.5279 0.50072.54 2.05 24% 14715 Metabolite - 3653 61 0.5332 0.5007 1.92 1.07 79%15000 Metabolite - 3758 61 0.5332 0.5007 0.96 0.91  5% 29817Metabolite - 10683 50 0.5335 0.5007 0.84 0.79  6% 19372 Metabolite -6269 50 0.535 0.5007 1.09 1.13 −4% 18091 Metabolite - 5306 61 0.53720.5007 1.06 0.96 10% 15676 3-methyl-2- 61 0.554 0.5065 0.93 0.89  4%oxovaleric acid 16468 Metabolite - 4236 61 0.5544 0.5065 1.1 0.98 12%22053 3-hydroxydecanoic 61 0.5575 0.5065 0.92 1.07 −14%  acid 10781Metabolite - 2469 61 0.5584 0.5065 0.8 1 −20%  17568 Metabolite - 493161 0.5585 0.5065 0.89 1.05 −15%  12625 Metabolite - 3002 50 0.56050.5065 1.74 1.78 −2% 1643 fumaric acid 50 0.5626 0.5065 0.88 0.96 −8%27272 Metabolite - 10505 50 0.5629 0.5065 0.72 0.74 −3% 19363Metabolite - 6227 50 0.5725 0.5111 0.83 0.79  5% 10655 Metabolite - 238861 0.5733 0.5111 0.93 1 −7% 58 glycine 50 0.5779 0.5111 0.9 0.98 −8%15128 DL-homocysteine 61 0.5816 0.5111 1.27 1.36 −7% 17068 Metabolite -4627 61 0.5821 0.5111 1.35 0.76 78% 1494 5-oxoproline 50 0.5828 0.51110.99 0.86 15% 6531 Metabolite - 1385 61 0.5892 0.5145 1.09 1.14 −4%19282 Metabolite - 6126 61 0.605 0.5238 1.38 1.03 34% 22130DL-3-phenyllactic 61 0.6056 0.5238 2.23 1.36 64% acid 1301 lysine 500.6074 0.5238 1.53 1.32 16% 6851 Metabolite - 1497 61 0.616 0.5277 1.551.46  6% 6398 Metabolite - 1335 61 0.6169 0.5277 1.34 1.56 −14%  27256Metabolite - 10500 50 0.6227 0.5304 1.1 1.05  5% 21841 Metabolite - 857761 0.6366 0.54 1.08 0.99  9% 5687 Metabolite - 1110 61 0.6394 0.54020.96 0.94  2% 12306 Metabolite - 2869 61 0.6482 0.5455 2.58 1.89 37%12767 Metabolite - 3087 50 0.6549 0.5488 1.44 1.18 22% 12638Metabolite - 3011 50 0.6612 0.5519 0.91 0.99 −8% 5489 Metabolite - 105761 0.6673 0.5547 1.03 1.05 −2% 19462 Metabolite - 6446 50 0.6727 0.55561.08 1.04  4% 10604 Metabolite - 2370 61 0.676 0.5556 0.86 0.88 −2%15121 Metabolite - 3786 61 0.6764 0.5556 0.86 0.89 −3% 10092Metabolite - 2250 61 0.6803 0.5561 1.63 0.76 114%  22320 Metabolite -8889 50 0.6822 0.5561 1.69 1.77 −5% 1645 n-dodecanoate 50 0.6858 0.55670.82 0.79  4% 527 lactate 50 0.6911 0.5589 0.99 0.91  9% 3127hypoxanthine 61 0.7003 0.5629 0.54 0.51  6% 24077 Metabolite - 9727 500.7015 0.5629 0.92 0.97 −5% 1366 trans-4- 50 0.7106 0.568 1.14 1.08  6%hydroxyproline 11499 Metabolite - 2753 61 0.7138 0.5684 1.31 0.8 64%18349 DL-indole-3-lactic 61 0.7229 0.5722 0.91 0.86  6% acid 7644Metabolite - 1831- 61 0.7346 0.5751 0.88 0.89 −1% 5531 Metabolite - 109561 0.7348 0.5751 1.34 1.34  0% 12768 Metabolite - 3088 50 0.736 0.57510.65 0.68 −4% 1431 p- 50 0.7405 0.5764 1.19 0.91 31% hydroxyphenyllacticacid 12673 Metabolite - 3040 50 0.7654 0.5878 0.75 0.82 −9% 2132citrulline 50 0.7659 0.5878 3.41 3.29  4% 22309 Metabolite - 8887 610.7694 0.5878 0.58 0.54  7% 30282 Metabolite - 10744 50 0.7699 0.58780.89 0.87  2% 14672 Metabolite - 3615 61 0.7721 0.5878 0.82 0.89 −8%25607 Metabolite - 10437 50 0.7765 0.589 0.76 0.87 −13%  6820Metabolite - 1554 61 0.7865 0.5944 1.06 1  6% 14239 Metabolite - 3474 610.7901 0.5949 1 0.9 11% 20248 Metabolite - 7177 61 0.7984 0.5973 1.041.02  2% 27326 Metabolite - 10527 50 0.799 0.5973 1.16 1.12  4% 25402Metabolite - 10360 50 0.8093 0.6029 1.29 1.3 −1% 19934 inositol 500.8135 0.6038 0.68 0.68  0% 12593 Metabolite - 2973 50 0.8339 0.61521.66 1.66  0% 23462 Metabolite - 9693 61 0.8348 0.6152 1.15 1.15  0%16819 Metabolite - 4496 50 0.8463 0.6215 0.88 0.85  4% 16138Metabolite - 4080 50 0.8667 0.6297 0.53 0.51  4% 25609 Metabolite -10439 50 0.8668 0.6297 0.72 0.66  9% 584 mannose 50 0.87 0.6297 0.930.84 11% 15122 glycerol 50 0.8737 0.6297 0.93 0.89  4% 22570Metabolite - 9033 50 0.875 0.6297 1.52 1.51  1% 20888 Metabolite - 780661 0.8756 0.6297 0.97 0.99 −2% 13038 Metabolite - 3143 61 0.8833 0.63161.32 1.14 16% 16509 Metabolite - 4273 50 0.8851 0.6316 0.69 0.7 −1%16511 Metabolite - 4274 50 0.8873 0.6316 1.1 1.08  2% 13589 Metabolite -3327 61 0.8935 0.6338 1.19 1.06 12% 5628 Metabolite - 1086 61 0.90020.6364 1.73 1.6  8% 10245 Metabolite - 2269- 61 0.9081 0.6398 1.08 0.8724% 6362 Metabolite - 1323- 61 0.9178 0.6429 1.08 1.02  6%possible-p-cresol- sulfate 5632 Metabolite - 1138 61 0.9201 0.6429 1.031.05 −2% 15365 sn-Glycerol-3- 50 0.9265 0.6429 0.82 0.82  0% phosphate10304 Metabolite - 2276 61 0.9274 0.6429 1.36 1.77 −23%  10156Metabolite - 2259 61 0.9281 0.6429 1.06 1.03  3% 27719 galactonic acid50 0.9386 0.6449 1.13 1.1  3% 22895 Metabolite - 9299 50 0.9394 0.64491.05 1.05  0% 15253 Metabolite - 3832 61 0.9468 0.6449 1.48 1.41  5%1105 Linoleic acid 50 0.9483 0.6449 0.95 0.94  1% 12780 Metabolite -3098 50 0.9494 0.6449 0.87 0.83  5% 10441 Metabolite - 2308 61 0.95060.6449 0.92 0.88  5% 27672 3-indoxyl-sulfate 61 0.9527 0.6449 0.98 0.95 3% 22133 DL-hexanoyl- 61 0.9585 0.646 1.36 1.35  1% carnitine 16070Metabolite - 4019 50 0.9631 0.646 0.85 0.85  0% 12720 Metabolite - 305661 0.9635 0.646 0.9 0.92 −2% 24074 Metabolite - 9706 50 0.9711 0.64791.12 1.11  1% 13545 Metabolite - 3322 61 0.9736 0.6479 1.49 1.34 11%1564 citric acid 50 0.9794 0.6479 3.72 3.73  0% 17327 Metabolite - 476750 0.9803 0.6479 1.32 1.03 28% 20950 Metabolite - 7846 50 0.982 0.64790.93 0.97 −4% 14837 Metabolite - 3707 61 0.99 0.6511 2.12 1.66 28%

Random forests were generated for plasma and serum biomarkers. Themodels for the serum biomarkers correctly classified 81.5% of thesubjects as either being healthy or having metabolic syndrome; 83% ofthe healthy subjects were classified correctly and 77% of the subjectshaving metabolic syndrome were correctly classified. For the modelsbased on the biomarkers from plasma, the 89% of the subjects werecorrectly classified as either being healthy or having metabolicsyndrome; 100% of the healthy subjects were correctly classified and 77%of the metabolic syndrome subjects were correctly classified. The mostimportant biomarkers are shown in the importance plot in FIG. 8 (Serum)and FIG. 9 (Plasma).

3B: Biomarkers of Atherosclerosis

Biomarkers were discovered by (1) analyzing plasma, aorta and liversamples drawn from subjects with atherosclerosis and healthy subjects todetermine the levels of metabolites in the samples and then (2)statistically analyzing the results to determine those metabolites thatwere differentially present in the two groups.

The samples used for the analysis were from wild-type and transgenicmice, C57BL/6 and LDb, respectively. The transgenic LDb mice provide amodel for atherosclerosis in human subjects. Previous studies have shownthat LDb transgenic mice in a C57BL/6 background have about 5-foldhigher plasma cholesterol and triglyceride levels than C57BL/6 wild-typemice and start to develop atherosclerotic lesions at about 3 months ofage. Plasma, ascending and descending aorta tissues and liver tissuefrom each group of mice at 2, 5 or 8 months were subjected tometabolomic analysis. These collection time points represent early(initiation), mid and late stage for atherosclerosis in the transgenicmodel.

T-tests were used to determine differences in the mean levels ofmetabolites between the two populations (i.e., LDb vs. C57BL/6).Classification analysis was carried out using recursive partitioning andrandom forest analyses to uncover the biomarkers that can bestdifferentiate the 2 groups of mice. Recursive partitioning relates a‘dependent’ variable (Y) to a collection of independent (‘predictor’)variables (X) in order to uncover—or simply understand—the elusiverelationship, Y=f(X). It was performed with the JMP program (SAS) togenerate a decision tree. The statistical significance of the “split” ofthe data can be placed on a more quantitative footing by computingp-values, which discern the quality of a split relative to a randomevent. The significance level of each “split” of data into the nodes orbranches of the tree was computed as p-values, which discern the qualityof the split relative to a random event. It was given as LogWorth, whichis the negative log 10 of a raw p-value. Statistical analyses wereperformed with the program “R” available on the worldwide web at thewebsite cran.r-project.org.

Random forests give an estimate of how well individuals can beclassified in a new data set into each group, in contrast to a t-test,which tests whether the unknown means for two populations are differentor not. Random forests create a set of classification trees based oncontinual sampling of the experimental units and compounds. Then eachobservation is classified based on the majority votes from all theclassification trees. Statistical analyses were performed with theprogram “R” available on the worldwide web at the websitecran.r-project.org.

Biomarkers:

As listed below in Tables 14, 15 and 16, biomarkers were discovered thatwere differentially present between samples from LDb (atherosclerotic)subjects and C57BL/6 (healthy) subjects.

Tables 14, 15 and 16 include, for each listed biomarker, the p-value andq-value determined in the statistical analysis of the data concerningthe biomarkers and an indication of the percentage difference in theatherosclerotic mean level as compared to the healthy mean level inplasma (Table 14), aorta (Table 15) and liver (Table 16). The term“Isobar” as used in the tables indicates the compounds that could not bedistinguished from each other on the analytical platform used in theanalysis (i.e., the compounds in an isobar elute at nearly the same timeand have similar (and sometimes exactly the same) quant ions, and thuscannot be distinguished). Comp_ID refers to the compound identificationnumber used as a primary key for that compound in the in-house chemicaldatabase. Library indicates the chemical library that was used toidentify the compounds. The number 50 refer to the GC library and thenumber 61 refers to the LC library.

TABLE 14 Metabolite biomarkers of Atherosclerosis in plasma. % ChangeCOMP_ID COMPOUND LIB_ID p-value q-value in LDb 21415 Metabolite - 820950 4.25E−07 2.40E−06 497% 21012 Metabolite - 7889 50 1.12E−22 6.95E−21494% 21011 Metabolite - 7888 50 8.87E−24 8.25E−22 482% 25649Metabolite - 10450 50 4.17E−15 7.76E−14 442% 18619 Metabolite - 5669 615.95E−06 2.84E−05 423% 27279 Metabolite - 10511 50 1.81E−19 6.74E−18404% 63 cholesterol 50 1.56E−24 2.90E−22 400% 8469 Metabolite -2036-possible- 61 1.00E−04 4.00E−04 371% Heme 27278 Metabolite - 1051050 5.00E−19 1.33E−17 351% 22993 Metabolite - 9448 50 4.73E−15 8.00E−14350% 25366 Metabolite - 10286 61 1.65E−05 1.00E−04 339% 21631Metabolite - 8403 50 9.85E−21 4.58E−19 306% 27414 beta-sitosterol 501.11E−12 1.29E−11 302% 21013 Metabolite - 7890 50 6.00E−14 7.73E−13 237%12785 Metabolite - 3103 50 0.0011 0.0026 236% 16831 Metabolite - 4504 502.00E−04 5.00E−04 234% 18155 Metabolite - 5386 61 2.55E−05 1.00E−04 223%8159 Metabolite - 1971 61 0.0026 0.0054 213% 27256 Metabolite - 10500 502.49E−19 7.72E−18 209% 21127 monopalmitin 50 6.23E−14 7.73E−13 204%21184 1-oleoyl-rac-glycerol 50 6.14E−08 4.17E−07 196% 6380 Metabolite -1330 61 0.0963 0.0914 177% 21188 1-stearoyl-rac-glycerol 50 5.43E−125.94E−11 177% 22032 Metabolite - 8766 50 1.79E−14 2.78E−13 163% 1561alpha-tocopherol 50 4.00E−04 0.001 161% 6266 Metabolite - 1286 616.00E−04 0.0015 130% 19323 Docosahexaenoic-Acid 50 2.43E−17 5.65E−16126% 27890 Metabolite - 10611 50 2.81E−09 2.26E−08 126% 6130Metabolite - 1208 61 1.04E−05 4.62E−05 124% 6362 p-cresol-sulfate 612.77E−05 1.00E−04 123% 9172 Metabolite - 2000 61 1.43E−15 2.96E−14 121%15991 L-alpha- 61 0.0014 0.0032 117% glycerophosphorylcholine 9905Metabolite - 2231 61 1.00E−04 2.00E−04 116% 15611 Metabolite - 3971 613.71E−06 1.82E−05 115% 24205 Metabolite - 9841 61 1.98E−06 1.02E−05 111%12604 Metabolite - 2981 50 6.77E−09 5.04E−08 111% 27888 Metabolite -10609 50 0.0116 0.0181 110% 27728 glycerol-2-phosphate 50 8.04E−063.65E−05 107% 12035 nonanate 50 3.95E−05 2.00E−04 104% 17251Metabolite - 4732 61 0.0089 0.0147 103% 23079 Metabolite - 9647 611.18E−05 1.00E−04 101% 1110 arachidonic acid 50 0.0015 0.0032 100% 15753hippuric acid 61 1.00E−04 2.00E−04 97% 17800 cortodoxone 61 1.00E−044.00E−04 92% 27773 Isobar-71^([1]) 61 0.0301 0.0379 92% 1359 oleic acid50 0.0669 0.0711 90% 1358 octadecanoic acid 50 4.62E−14 6.61E−13 88%12774 Metabolite - 3094 50 2.92E−09 2.26E−08 86% 12767 Metabolite - 308750 0.007 0.0124 85% 24330 Metabolite - 10126 61 1.00E−09 8.46E−09 84%21150 sinapic acid 61 1.10E−10 9.75E−10 84% 20136 Metabolite - 7062 613.65E−05 1.00E−04 80% 10782 Metabolite - 2486 61 0.0854 0.0837 80% 6171Metabolite - 1244 61 0.003 0.0059 78% 16138 Metabolite - 4080 503.02E−07 1.76E−06 77% 21418 Isobar-56^([2]) 61 6.28E−08 4.17E−07 77%1105 linoleic acid 50 3.36E−11 3.29E−10 75% 1336 n-hexadecanoic acid 503.99E−11 3.71E−10 73% 12112 Metabolite - 2314 61 1.47E−06 7.82E−06 72%57 glutamic acid 50 0.0083 0.0141 71% 22586 Metabolite - 9039 611.00E−04 3.00E−04 70% 21828 Metabolite - 8574 61 0.0027 0.0055 68% 26444Metabolite - 10465 61 2.89E−11 2.99E−10 66% 5475 Metabolite - 1033 611.01E−07 6.06E−07 63% 10700 Metabolite - 2393 61 0.0101 0.0162 61% 22259Isobar-59^([3]) 61 0.0013 0.003 58% 16992 Metabolite - 4603 61 0.02370.031 57% 18691 Metabolite - 5749 61 0.0854 0.0837 57% 19934 inositol 500.0067 0.012 55% 12609 Metabolite - 2986 50 0.0013 0.0029 55% 15365sn-Glycerol-3-phosphate 50 8.00E−04 0.0018 53% 11499 Metabolite - 275361 0.0071 0.0125 51% 16074 Metabolite - 2758 50 6.00E−04 0.0016 49%15670 2-methylhippuric acid 61 6.00E−04 0.0016 49% 8210 Metabolite -1981 61 0.017 0.0238 47% 1121 heptadecanoic acid 50 8.42E−09 6.03E−0846% 17488 Metabolite - 4887 61 2.00E−04 6.00E−04 45% 1493 ornithine 500.0482 0.0557 42% 6851 Metabolite - 1497 61 0.0676 0.0711 42% 22895Metabolite - 9299 50 0.0133 0.0195 39% 13038 Metabolite - 3143 61 0.01170.0181 39% 5699 Metabolite - 1157 61 0.0036 0.0068 37% 18015Metabolite - 3113 61 1.00E−04 4.00E−04 36% 24076 Metabolite - 9726 503.00E−04 0.001 36% 20031 Metabolite - 7007 61 0.0011 0.0026 36% 20488D-glucose 50 2.00E−04 7.00E−04 34% 10401 Metabolite - 2058 61 0.00150.0032 34% 1507 palmitoleic acid 50 0.0168 0.0236 34% 1670 urea 504.00E−04 0.001 34% 15948 S-adenosyl-l-homocysteine 61 0.023 0.0303 33%5465 Metabolite - 1029 61 8.00E−04 0.002 33% 2849 guanosine-5- 61 0.04180.0499 33% monophosphate 1494 5-oxoproline 50 0.0039 0.0073 32% 11923Metabolite - 2821 61 0.0089 0.0147 32% 18969 Metabolite - 5920 61 0.00330.0063 32% 20830 Metabolite - 7762 61 0.056 0.0621 31% 2832 adenosine-5-61 0.0137 0.0199 30% monophosphate 17083 Metabolite - 4634 50 0.07180.0742 28% 15996 aspartate 50 0.1053 0.0995 27% 14639 Metabolite - 360361 0.0225 0.0302 26% 25514 Metabolite - 10404 61 0.0747 0.0768 26% 17747D-sphingosine 50 0.0239 0.0311 26% 27137 Metabolite - 10498 50 0.05110.0576 26% 30128 Metabolite - 10687 61 1.00E−04 4.00E−04 25% 16244Isobar-21^([4]) 61 0.0038 0.0072 25% 15053 sorbitol 50 0.0718 0.0742 25%15122 glycerol 50 0.0019 0.004 25% 14387 Metabolite - 3490 61 0.07540.0771 23% 1642 decanoic acid 50 0.0455 0.0536 23% 206751,5-anhydro-D-glucitol 50 0.1081 0.1016 22% 11438 phosphate 50 0.00220.0045 21% 584 mannose 50 0.0128 0.0191 19% 2129 L-5-Hydroxytryptophan61 0.0809 0.0806 19% 9491 Metabolite - 2185 61 0.0162 0.023 18% 605uracil 50 0.0468 0.0544 18% 10655 Metabolite - 2388 61 0.0112 0.0177 18%17665 p-hydroxybenzaldehyde 61 0.0121 0.0186 18% 10746 Isobar-6^([5]) 610.0564 0.0621 16% 19372 Metabolite - 6269 50 0.0117 0.0181 16% 9216Metabolite - 2168 61 0.0464 0.0543 15% 15113 Metabolite - 3783 61 0.01260.019 14% 27718 creatine 61 0.0245 0.0312 14% 1113 isocitrate 61 0.01320.0195 14% 18232 Metabolite - 5403 50 0.0493 0.0562 14% 10737Isobar-1^([6]) 61 0.0229 0.0303 13% 7175 Metabolite - 1655 61 0.00990.016 13% 19402 Metabolite - 6346 50 0.0411 0.0496 10% 156773-methyl-L-histidine 61 0.0795 0.0799 −8% 9324 Metabolite - 2173 610.0842 0.0834 −9% 54 tryptophan 61 0.0808 0.0806 −11% 17007 Metabolite -4609 61 0.0957 0.0914 −11% 1574 histamine 61 0.0574 0.0629 −12% 5765Metabolite - 1142 61 0.0951 0.0913 −14% 27672 3-indoxyl-sulfate 610.0514 0.0576 −15% 10667 Metabolite - 2389 61 0.0939 0.0906 −15% 15765ethylmalonic acid 61 0.0241 0.0311 −17% 16705 Metabolite - 4428 610.0153 0.0219 −17% 15529 Metabolite - 3951 61 0.0125 0.019 −18% 11053Metabolite - 2567 61 0.0053 0.0098 −18% 24213 Metabolite - 9845 610.0189 0.0261 −19% 20950 Metabolite - 7846 50 0.0915 0.0887 −19% 13065Metabolite - 3146 61 0.0021 0.0045 −20% 1643 fumaric acid 50 0.04850.0558 −20% 1512 picolinic acid 50 0.0322 0.0396 −21% 24197 Metabolite -9838 61 0.0675 0.0711 −22% 12724 Metabolite - 3057 61 0.0174 0.0241 −22%27130 Metabolite - 10493 61 0.0087 0.0146 −22% 156763-methyl-2-oxovaleric acid 61 0.0716 0.0742 −23% 18871 Metabolite - 584861 0.0385 0.0468 −24% 13328 Metabolite - 3238 61 0.0194 0.0266 −24% 6253Metabolite - 1283 61 0.0595 0.0644 −25% 10347 Metabolite - 2285 610.0015 0.0032 −25% 16712 Metabolite - 4432 61 0.0081 0.0139 −26% 14786Metabolite - 3697 61 0.0795 0.0799 −27% 15683 4-methyl-2-oxopentanoate61 0.0128 0.0191 −28% 8644 Metabolite - 2051 61 0.0413 0.0496 −28% 7650Metabolite - 1834 61 7.29E−08 4.52E−07 −28% 6398 Metabolite - 1335 610.0617 0.0664 −29% 1412 2′-deoxyuridine 61 4.00E−04 0.001 −30% 19405Metabolite - 6347 50 0.0877 0.0855 −31% 22130 DL-3-phenyllactic acid 610.0441 0.0522 −34% 26449 Metabolite - 10467 61 0.0777 0.079 −34% 25584Metabolite - 10425 50 0.0078 0.0136 −34% 18929 Metabolite - 5907 500.0253 0.032 −35% 28059 Metabolite - 10650 50 1.00E−04 5.00E−04 −36%5466 Metabolite - 1030 61 0.0098 0.016 −37% 20169 Metabolite - 7092 610.0582 0.0633 −37% 25459 Metabolite - 10395 50 0.0542 0.0604 −38% 17091Metabolite - 4641 61 8.22E−07 4.50E−06 −38% 19968 Metabolite - 6930 500.0318 0.0395 −39% 11299 Metabolite - 2706 61 6.83E−08 4.38E−07 −39%25505 Metabolite - 10402 61 4.00E−04 0.0012 −39% 16653 Metabolite - 436150 0.0032 0.0063 −41% 18968 Metabolite - 5919 61 4.00E−04 0.0011 −41%11292 Metabolite - 2703 61 0.0059 0.0107 −41% 13534 Metabolite - 3320 610.0055 0.01 −43% 6297 Metabolite - 1304 61 0.0334 0.0409 −43% 19367Metabolite - 6266 50 0.0035 0.0068 −44% 16071 Metabolite - 4020 501.00E−04 4.00E−04 −45% 24206 Metabolite - 9842 61 0.0142 0.0204 −47%25602 Metabolite - 10432 50 8.00E−04 0.0018 −52% 25598 Metabolite -10428 50 0.0665 0.0711 −58% 25597 Metabolite - 10427 50 0.0028 0.0056−58% 25599 Metabolite - 10429 50 0.0319 0.0395 −59% 22548 Metabolite -9026 50 0.0202 0.0274 −61% 19362 Metabolite - 6226 50 0.0243 0.0312 −65%16650 Metabolite - 4360 50 0.008 0.0138 −66% 25538 Metabolite - 10415 610.0495 0.0562 −66% 25546 Metabolite - 10418 61 0.0018 0.0038 −73% 25527Metabolite - 10410 61 2.00E−04 5.00E−04 −78% 22566 Metabolite - 9029 612.00E−04 6.00E−04 −80% 25529 Metabolite - 10411 61 3.00E−04 0.001 −81%25517 Metabolite - 10406 61 6.00E−04 0.0016 −83% 10148 Metabolite - 225761 1.00E−04 5.00E−04 −84% 21651 Metabolite - 8410 61 6.66E−06 3.10E−05−87% 25541 Metabolite - 10417 61 2.84E−05 1.00E−04 −91% 25539Metabolite - 10416 61 3.59E−06 1.81E−05 −93% ^([1])Isobar-71 includesconduritol-beta-epoxide-3-deoxyglucosone ^([2])Isobar-56 includesDL-pipecolic acid-1-amino-1-cyclopentanecarboxylic acid ^([3])Isobar-59includes N-6-trimethyl-L-lysine-H-homoarg-OH ^([4])Isobar-21-includesgamma-aminobutyryl-L-histidine-L-anserine ^([5])Isobar-6 includesvaline-betaine ^([6])Isobar-1 includes mannose, fructose, glucose,galactose, alpha-L-sorbopyranose, Inositol, D-allose, D-altrose,D-psicone, L-gulose, allo-inositol

TABLE 15 Metabolite biomarkers of Atherosclerosis in aorta. % ChangeCOMP_ID COMPOUND LIB_ID p-value q-value in LDb 63 cholesterol 507.16E−07 8.78E−06 569% 22993 Metabolite-9448 50 1.06E−05 1.00E−04 565%10655 Metabolite-2388 61 2.34E−07 4.83E−06 287% 25548 Metabolite-1041950 1.00E−04 3.00E−04 277% 22320 Metabolite-8889 50 0.0015 0.0018 244%15991 L-alpha- 61 0.0174 0.0135 227% glycerophosphorylcholine 27256Metabolite-10500 50 6.36E−07 8.78E−06 190% 19383 Metabolite-6286 506.00E−04 0.001 125% 9137 Metabolite-2141 61 0.0026 0.0028 121% 16028Metabolite-3998 50 0.0012 0.0015 110% 10739 Metabolite-2407 61 0.00570.0053 94% 17987 Metabolite-5228 50 0.0087 0.0078 92% 22675Metabolite-9126 61 9.00E−04 0.0013 89% 1481 inositol-1-phosphate 500.0149 0.0117 78% 30173 Metabolite-10701 61 0.002 0.0022 70% 12774Metabolite-3094 50 0.0252 0.0186 66% 21421 Metabolite-8214 50 0.04820.0321 63% 22032 Metabolite-8766 50 0.0017 0.0019 62% 12604Metabolite-2981 50 0.0027 0.0028 54% 16002 Metabolite-3992[1] 611.71E−05 1.00E−04 37% 20360 Metabolite-7326 61 0.0352 0.0243 −14% 7601Metabolite-1819 61 0.0351 0.0243 −14% 18829 phenylalanine 61 0.03030.0216 −16% 1604 uric acid 50 0.0331 0.0234 −17% 13505 Metabolite-331361 0.0337 0.0237 −17% 19787 Metabolite-6746 61 0.0143 0.0115 −18% 16235Isobar-19^([2]) 61 0.0433 0.0292 −18% 10743 Isobar-4^([3]) 61 0.04280.0292 −18% 54 tryptophan 61 0.0217 0.0164 −19% 3147 xanthine 61 0.02340.0175 −19% 27570 Metabolite-10569 61 0.0177 0.0135 −20% 15529Metabolite-3951 61 0.0468 0.0313 −20% 1125 isoleucine 50 0.0109 0.0092−22% 1648 serine 50 0.0071 0.0065 −23% 605 uracil 50 0.0416 0.0286 −23%7029 Metabolite-1597 61 0.0045 0.0043 −24% 1299 tyrosine 61 0.008 0.0073−24% 1592 N-acetylneuraminic acid 61 0.0087 0.0078 −24% 1508 pantothenicacid 61 0.0105 0.0089 −24% 13810 Metabolite-3379 61 0.0146 0.0116 −24%24076 Metabolite-9726 50 1.00E−04 3.00E−04 −26% 8336 Metabolite-2005 610.0019 0.0021 −26% 22258 Isobar-58^([4]) 61 0.0292 0.0211 −26% 60leucine 50 0.0045 0.0043 −28% 30128 Metabolite-10687 61 0.0114 0.0095−28% 17048 Metabolite-4617 61 0.0014 0.0017 −29% 17960 Metabolite-520750 0.0015 0.0018 −29% 15677 3-methyl-L-histidine 61 5.00E−04 0.001 −30%1431 (p-Hydroxyphenyl)lactic acid 50 0.001 0.0013 −30% 1649 valine 500.0039 0.004 −30% 1898 proline 50 0.0042 0.0042 −30% 1284 threonine 500.0047 0.0045 −31% 15948 S-adenosyl-l-homocysteine 61 0.0101 0.0088 −31%26456 Metabolite-10470 61 0.0134 0.011 −31% 1302 methionine 61 0.01610.0125 −31% 22185 n-acetyl-l-aspartic acid 61 9.00E−04 0.0013 −32% 14143-phospho-d-glycerate 61 0.002 0.0022 −32% 1643 fumaric acid 50 0.00420.0042 −33% 10890 Metabolite-2554 61 0.0029 0.003 −34% 10746Isobar-6^([5]) 61 0.0283 0.0208 −35% 15253 Metabolite-3832-possible- 610.0292 0.0211 −35% phenol-sulfate 5821 3-phospho-l-serine 61 7.00E−040.0012 −36% 12102 o-phosphoethanolamine 50 0.0014 0.0018 −36% 8404Metabolite-2027 61 0.0019 0.0021 −36% 1123 inosine 61 2.00E−04 5.00E−04−37% 3127 hypoxanthine 61 8.00E−04 0.0012 −37% 6771 Metabolite-1460 610.0015 0.0018 −37% 14311 Metabolite-3481 61 0.0198 0.015 −37% 16233Isobar-13^([6]) 61 6.00E−04 0.001 −38% 11222 Metabolite-2688 61 5.00E−040.001 −38% 1638 arginine 61 8.00E−04 0.0012 −38% 15497arginino-succinate 61 0.0012 0.0015 −38% 15506 choline 61 2.00E−045.00E−04 −39% 16705 Metabolite-4428 61 0.0012 0.0016 −39% 8991Metabolite-2105 61 0.0096 0.0085 −39% 514 cytidine 61 5.00E−04 9.00E−04−40% 13018 Metabolite-3138 61 5.00E−04 0.001 −40% 527 lactate 507.00E−04 0.0012 −40% 23051 Metabolite-9566 61 0.001 0.0014 −40% 28131Metabolite-10670 61 4.00E−04 8.00E−04 −41% 1494 5-oxoproline 50 9.00E−040.0013 −41% 606 uridine 61 5.00E−04 0.001 −42% 18374methionine-sulfoxide 61 1.00E−04 4.00E−04 −43% 57 glutamic acid 506.00E−04 0.001 −44% 15996 aspartate 50 8.00E−04 0.0012 −44% 20489D-glucose 50 0.0051 0.0048 −44% 18348 3-hydroxy-3methylglutaryl- 61 0.010.0087 −44% coenzyme-A 27718 creatine 61 2.00E−04 5.00E−04 −45% 1303malic acid 61 7.00E−04 0.0011 −45% 1412 2′-deoxyuridine 61 1.02E−061.09E−05 −46% 21430 Metabolite-8266 61 2.00E−04 6.00E−04 −47% 11544Metabolite-2766 61 0.0013 0.0016 −47% 19372 Metabolite-6269 50 0.00270.0028 −47% 22145 acetyl-L-carnitine 61 2.00E−04 5.00E−04 −48% 10737Isobar-1^([7]) 61 3.00E−04 6.00E−04 −48% 14247 Metabolite-3475 61 0.01460.0116 −48% 19110 Metabolite-5978 50 3.00E−04 7.00E−04 −49% 22730Metabolite-9186 61 8.00E−04 0.0012 −49% 11777 glycine 50 5.00E−04 0.001−50% 16228 Isobar-22^([8]) 61 1.00E−04 3.00E−04 −51% 16843Metabolite-4510 50 1.00E−04 3.00E−04 −51% 53 glutamine 50 2.00E−046.00E−04 −51% 19708 Metabolite-6711 61 0.0124 0.0103 −51% 1416 GABA 501.38E−08 1.18E−06 −52% 15500 carnitine 61 5.00E−04 9.00E−04 −52% 2125taurine 61 0.0017 0.002 −52% 22475 Metabolite-8986 61 0.0044 0.0043 −52%28059 Metabolite-10650 50 1.87E−05 1.00E−04 −54% 20361 Metabolite-732761 3.32E−05 2.00E−04 −54% 17971 Metabolite-5210 50 1.00E−04 3.00E−04−54% 22494 Metabolite-8994 50 4.00E−04 9.00E−04 −54% 7650Metabolite-1834 61 2.06E−07 4.83E−06 −55% 27738 threonic acid 501.00E−04 3.00E−04 −55% 27678 Metabolite-10584 50 3.00E−04 7.00E−04 −55%12459 Isobar-10^([9]) 61 3.43E−05 2.00E−04 −56% 15125(2-Aminoethyl)phosphonate 61 1.00E−04 3.00E−04 −56% 19934 inositol 503.00E−04 7.00E−04 −56% 16860 Metabolite-4517 50 1.00E−04 3.00E−04 −57%17064 Metabolite-4624 50 1.00E−04 4.00E−04 −57% 1107 allantoin 505.00E−04 0.001 −57% 1573 guanosine 61 4.00E−06 3.69E−05 −58% 1670 urea50 1.00E−04 3.00E−04 −58% 27727 glutathione-oxidized 61 1.00E−044.00E−04 −61% 1519 sucrose 50 0.0104 0.0089 −62% 22702 Metabolite-912761 4.30E−06 3.69E−05 −63% 15336 tartaric acid 61 6.92E−08 2.97E−06 −66%6172 Metabolite-1245 61 2.82E−07 4.83E−06 −70% 17975 Metabolite-5211 501.04E−05 1.00E−04 −70% 27773 Isobar-71^([10]) 61 3.00E−04 7.00E−04 −71%[1] Possibly Cl-adduct of Formate dimmer ^([2])Isobar-19 includes1,5-anhydro-D-glucitol, 2′-deoxy-D-galactose, 2′-deoxy-D-glucose,L-fucose, L-rhamnose ^([3])Isobar-4 includes Gluconic acid,DL-arabinose, D-ribose, L-xylose, DL-lyxose, D-xylulose, galactonic acid^([4])Isobar-58 includes bicine, 2-methylaminomethyl-tartronic acid^([5])Isobar-6 includes valine-betaine ^([6])Isobar-13 includes5-keto-D-gluconic acid, 2-keto-L-gulonic acid, D-glucuronic acid,D-galacturonic acid ^([7])Isobar-1 includes mannose, fructose, glucose,galactose, alpha-L-sorbopyranose, Inositol, D-allose, D-altrose,D-psicone, L-gulose, allo-inositol ^([8])Isobar-22-includes-glutamicacid-O-acetyl-L-serine^([9])Isobar-10-includes-glutamine-H-beta-ala-gly-OH-1-methylguanine-H-Gly-Sar-OH-lysine^([10])Isobar-71includes conduritol-beta-epoxide-3-deoxyglucosone

TABLE 16 Metabolite biomarkers of Atherosclerosis in liver. % COMP_IDCOMPOUND LIB_ID p-value q-value Change in LDb 19788 Metabolite-6747 616.49E−06 0.001 895% 1564 citric acid 50 0.0069 0.0454 399% 9117Metabolite-2135 61 0.0173 0.0851 206% 25626 Metabolite-10443 61 0.0210.0964 180% 19597 Metabolite-6648 50 0.0055 0.0452 162% 156855-hydroxylysine 61 0.0049 0.0452 159% 16655 Metabolite-4362 50 0.00640.0454 131% 21418 Isobar-56^([1]) 61 1.36E−05 0.0011 129% 22475Metabolite-8986 61 0.0012 0.0191 129% 15803 maltose 50 0.0224 0.0992128% 18344 D-xyulose 50 1.00E−04 0.0071 126% 27678 Metabolite-10584 500.0083 0.049 126% 22020 Metabolite-8749 50 6.00E−04 0.0168 124% 30204Metabolite-10713 61 0.0138 0.0703 122% 30203 Metabolite-10712 619.00E−04 0.0186 113% 15053 sorbitol 50 0.0048 0.0452 105% 1640 ascorbicacid 50 0.0032 0.0375 104% 19753 Metabolite-6718 61 0.0073 0.046 97%21650 Metabolite-8409 61 0.0185 0.0866 91% 1516 sarcosine 50 0.01240.0659 85% 22309 Metabolite-8887 61 0.001 0.0191 85% 27299Metabolite-10520 61 4.00E−04 0.0136 83% 1118 eicosanoic acid 50 0.00150.0191 78% 25429 Metabolite-10369 50 0.0045 0.0452 72% 8669Metabolite-2055 61 0.0087 0.0498 69% 8210 Metabolite-1981 61 0.0230.0992 65% 20488 D-glucose 50 0.0073 0.046 55% 15606 Metabolite-3968 610.0069 0.0454 51% 11379 Metabolite-2725 61 0.0224 0.0992 50% 11484Metabolite-2752 61 7.00E−04 0.0168 50% 11292 Metabolite-2703 61 0.01330.0694 49% 8457 Metabolite-2035[2] 61 0.0066 0.0454 47% 16859Metabolite-4516 50 0.0066 0.0454 44% 554 adenine 50 0.0076 0.0463 44%7081 Metabolite-1609 61 0.0039 0.0412 41% 20795 Metabolite-7747 610.0033 0.0375 40% 16229 Isobar-24^([3]) 61 0.0055 0.0452 39% 18388Metabolite-5491 50 0.0057 0.0452 39% 24360 Metabolite-10206 50 0.01780.086 38% 12080 D-ribose 50 0.0104 0.0575 37% 22993 Metabolite-9448 500.0156 0.0782 36% 10737 Isobar-1^([4]) 61 0.0084 0.049 36% 16060Metabolite-4014 50 0.0181 0.0862 36% 24285 Metabolite-10026 61 0.02290.0992 23% 63 cholesterol 50 0.0104 0.0575 15% 22414 Metabolite-8933 610.0037 0.0406 −29% 1827 riboflavine 61 0.0052 0.0452 −29% 22320Metabolite-8889 50 0.0056 0.0452 −29% 9002 Metabolite-2107 61 0.00120.0191 −31% 3138 pyridoxamine-phosphate 61 0.0054 0.0452 −34% 15964D-arabitol 50 2.00E−04 0.0098 −34% 7432 Metabolite-1735 61 0.0014 0.0191−35% 22185 n-acetyl-l-aspartic acid 61 0.0068 0.0454 −36% 23024Metabolite-9458 61 0.0011 0.0191 −37% 21296 glucosamine-6-sulfate 610.0014 0.0191 −39% 7650 Metabolite-1834 61 7.40E−06 0.001 −41% 9468Metabolite-2183 61 8.00E−04 0.0182 −46% 6530 Metabolite-1384 61 0.01150.0621 −57% 25561 Metabolite-10421 61 0.0069 0.0454 −58% 597phosphoenolpyruvate 61 0.0014 0.0191 −59% 1414 3-phospho-d-glycerate 501.57E−05 0.0011 −60% 10148 Metabolite-2257 61 0.003 0.037 −68% 27794Metabolite-10587 61 4.00E−04 0.0127 −69% 6146 alpha-amino-adipate 502.00E−04 0.0098 −73% ^([1])Isobar-56 includes DL-pipecolicacid-1-amino-1-cyclopentanecarboxylic acid [2] Possible5-methyl-deoxycytidine-monophosphate ^([3])Isobar-24 includesL-arabitol, adonitol, xylitol ^([4])Isobar-1 includes mannose, fructose,glucose, galactose, alpha-L-sorbopyranose, Inositol, D-allose,D-altrose, D-psicone, L-gulose, allo-inositol

Identification of plasma biomarkers indicative of initiation and/orprogression of atherosclerosis would help diagnosis and treatment ofhuman patients with this disease. Recursive partitioning of plasmametabolites identified cholesterol as a biomarker that coulddifferentiate the LDb and C57BL/6 mice perfectly, as expected. Threeother metabolites were also identified by recursive partitioning todifferentiate the LDb and C57BL/6 mice (Table 17). Plasma levels ofthese metabolites, like plasma cholesterol, were higher in LDb mice evenat 2 months of age and remained consistently higher during the following6 months, suggesting that earlier buildup of these metabolites is likelyinvolved in the development of atherosclerosis and provide biomarkersfor progression (FIGS. 13, 14, 15, and 16).

TABLE 17 Atherosclerosis biomarkers in plasma that differentiateAtherosclerosis subjects (LDb) and healthy control subjects (C57BL/6mice) without error. Atherosclerosis Control R- Cmpd_ID Compound levellevel Square LogWorth 19323 Docosahexaenoic- ≧528666 <528666 1.000 21.11Acid 21011 Metabolite-7888 ≧2523794 <2523794 1.000 21.11 21631Metabolite-8403 ≧238349 <238349 1.000 21.11

Metabolite-1834 did not segregate LDb and C57BL/6 groups in 2-month-oldmice, but started to segregate the 5-month-olds and segregated8-month-old mice perfectly (FIG. 17). This metabolite is one of thebiomarkers for atherosclerosis progression.

Several metabolites (p-cresol-sulfate, Metabolite-4887, Metabolite-5386)classified subjects as LDb or C57BL/6 very well in 2-month-old mice, butthe power of differentiation diminished as the mice aged, with nosegregation in 8-month-old mice (FIGS. 18 and 19). These metabolites arebiomarkers for atherosclerosis initiation.

Random forest results show that the samples can be classified correctlywith varying degrees of accuracy using the biomarkers. The confusionmatrices demonstrate that LDb subjects can be distinguished from C57BL/6subjects using plasma (Table 18), aorta (Table 19) and liver (Table 20)samples. The “Out-of-Bag” (OOB) Error rate gives an estimate of howaccurately new observations can be predicted using the random forestmodel (e.g., whether a sample is from a subject having atherosclerosisor a control subject).

TABLE 18 Random Forest Confusion Matrices for Atherosclerosis in PlasmaPlasma Control Atherosclerosis Error Age Collected: 2 months Control 100 0 Atherosclerosis 0 10  0 OOB Error 0 0% Age Collected: 5 monthsControl 10 0 0 Atherosclerosis 2 9 0 OOB Error 0 0% Age Collected: 8months Control 8 0 0 Atherosclerosis 0 8 0 OOB Error 0 0% ALL Control 280 0 Atherosclerosis 0 27  0 OOB Error 0 0%

TABLE 19 Random Forest Confusion Matrices for Atherosclerosis in AortaTissues Aorta Tissue Control Atherosclerosis Error Age Collected: 2months Control 6 1 0.14 Atherosclerosis 2 2 0.5 OOB Error 3/11 = 0.2727% Age Collected: 5 months Control 7 0 0 Atherosclerosis 2 4 0.33 OOBError 2/13 = 0.15 15% Age Collected: 8 months Control 6 0 0Atherosclerosis 0 4 0 OOB Error 0/10 = 0.00  0% ALL Control 18  2 0.1Atherosclerosis 3 11  0.21 OOB Error 5/34 = 0.15 15%

TABLE 20 Random Forest Confusion Matrices for Atherosclerosis in LiverLiver Control Atherosclerosis Error Age Collected: 2 months Control 7 10.13 Atherosclerosis 1 3 0.25 OOB Error 2/12 = 0.17 17% Age Collected: 5months Control 3 1 0.25 Atherosclerosis 3 3 0.5 OOB Error 4/10 = 0.4 40%Age Collected: 8 months Control 5 0 0 Atherosclerosis 1 5 0.17 OOB Error1/11 = 0.09  9% ALL Control 15  2 0.12 Atherosclerosis 3 13  0.19 OOBError 5/33 = 0.15 15%

In addition, a study was carried out on human subjects suffering fromatherosclerosis (n=15) or healthy subjects (n=14). The biomarkers3-methylhistidine, p-cresol sulfate, mannose, glucose, and gluconateshowed the same alterations in human plasma from disease vs. healthysubjects as seen in the mouse model. Thus, these compounds wereidentified as important biomarkers useful to distinguish individualswith atherosclerosis from healthy subjects.

3C: Biomarkers of Cardiomyopathy

Biomarkers were discovered by (1) analyzing cardiac tissue samples(Table 21) or plasma samples (Table 22) from different groups of mousesubjects to determine the levels of metabolites in the samples and then(2) statistically analyzing the results to determine those metabolitesthat were differentially present in the two groups. These subjectsprovide an animal (mouse) model for human DCM.

Two groups of subjects were used. One group consisted of eight subjectsexhibiting cardiac dilatation and depressed left ventricular systolicfunction (ejection fraction of less than 0.40), as determined byechocardiography (cTnT-W141 transgenic mice). Thirteen age- andgender-matched subjects (non-transgenic (wild-type background strain)mice) served as controls. All mice were 7-19 months old and weighed23-40 gm.

T-tests were used to determine differences in the mean levels ofmetabolites between the two populations (i.e., Dilated Cardiomyopathy,DCM vs. Healthy control). Classification analysis was carried out usingrecursive partitioning and random forest analyses to uncover thebiomarkers that can best differentiate the two groups. Recursivepartitioning relates a ‘dependent’ variable (Y) to a collection ofindependent (‘predictor’) variables (X) in order to uncover—or simplyunderstand—the elusive relationship, Y=f(X). It was performed with theJMP program (SAS) to generate a decision tree. The statisticalsignificance of the “split” of the data can be placed on a morequantitative footing by computing p-values, which discern the quality ofa split relative to a random event. The significance level of each“split” of data into the nodes or branches of the tree was computed asp-values, which discern the quality of the split relative to a randomevent. It was given as LogWorth, which is the negative log 10 of a rawp-value.

Biomarkers:

As listed below in Tables 21 and 22, biomarkers were discovered thatwere differentially present between cardiac tissue and plasma samples,respectively, collected from dilated cardiomyopathy subjects and healthysubjects.

Tables 21 and 22 include, for each listed biomarker, the p-value andq-value determined in the statistical analysis of the data concerningthe biomarkers and an indication of the percentage difference in thedilated cardiomyopathy mean level as compared to the healthy mean levelin cardiac tissue (Table 21) or plasma (Table 22). The term “Isobar” asused in the tables indicates the compounds that could not bedistinguished from each other on the analytical platform used in theanalysis (i.e., the compounds in an isobar elute at nearly the same timeand have similar (and sometimes exactly the same) quant ions, and thuscannot be distinguished). Comp_ID refers to the compound identificationnumber used as a primary key for that compound in the in-house chemicaldatabase. Library indicates the chemical library that was used toidentify the compounds. The number 50 refer to the GC library and thenumber 61 refers to the LC library.

TABLE 21 Metabolite biomarkers of dilated cardiomyopathy (DCM) incardiac tissues. % Change COMP_ID COMPOUND LIB_ID p-value q-value in DCM22185 n-acetyl-l-aspartic acid 61 0.0044 0.0144 100% 15996 aspartate 500.0002 0.0022 64% 1414 3-phospho-d-glycerate 50 0.0742 0.0888 52% 1898proline 61 3.64E−05 0.0007 52% 1648 serine 50 0.0009 0.0057 49% 1299tyrosine 61 3.36E−05 0.0007 39% 1284 threonine 50 0.0054 0.0173 28% 54tryptophan 61 0.0383 0.0571 20% 1649 valine 50 0.0081 0.0227 19% 1125isoleucine 50 0.0041 0.014 16% 11777 glycine 50 0.0109 0.0255 15% 13179creatine 61 0.0011 0.0061 −17% 590 hypotaurine 61 0.0728 0.0879 −21%5278 beta-nicotinamide adenine 61 0.0092 0.0239 −27% dinucleotide 15500carnitine 61 0.0331 0.0509 −32% 2127 glutathione, reduced 61 0.00330.0125 −62% 12080 D-ribose 50 0.0033 0.0125 39% 15122 glycerol 50 0.06630.0819 −16% 19934 inositol 50 0.0225 0.0417 −22% 18882 taurodeoxycholicacid 61 0.0584 0.0754 72% 15365 sn-Glycerol 3-phosphate 50 0.0109 0.025541% 63 cholesterol 50 0.011 0.0255 14% 1121 heptadecanoic acid 50 0.05940.0761 −15% 21127 monopalmitin 50 0.0149 0.0324 −23% 1336 n-hexadecanoicacid 50 0.0014 0.0072 −24% 19323 docosahexaenoic acid 50 0.0009 0.0057−24% 1600 o-phosphoethanolamine 50 0.0253 0.0435 −30% 1365 tetradecanoicacid 50 0.0516 0.0691 −31% 1570 oleic acid 50 0.0269 0.0446 −31% 1105linoleic acid 50 0.0014 0.0072 −32% 1518 squalene 50 0.0013 0.0072 −43%15504 phosphopantheine 61 0.0186 0.0367 −49% 1827 riboflavine 61 0.00370.0129 85% 594 niacinamide 50 0.0091 0.0239 −17% 3138 pyridoxaminephosphate 61 0.0047 0.0153 −19% 1508 pantothenic acid 61 0.0093 0.0239−32% 3127 hypoxanthine 50 0.0112 0.0255 92% 606 uridine 61 0.0262 0.044556% 1107 allantoin 50 0.0207 0.0398 39% 514 cytidine 61 0.0034 0.012633% 1573 guanosine 61 0.0326 0.0507 32% 605 uracil 50 0.0291 0.0457 27%21031 hydroxyurea 61 0.0069 0.0204 −47% 2856 uridine 5′-monophosphate 610.0005 0.004 −68% 18360 adenylosuccinic acid 61 9.02E−06 0.0004 −85% 555adenosine 61 0.0007 0.0051 −87% 2832 adenosine 5′-monophosphate 614.53E−06 0.0004 −90% 2849 guanosine 5′-monophosphate 61 2.06E−05 0.0006−94% 20701 malitol 50 0.0167 0.0343 −32% 8469 Metabolite-2036 61 0.01440.0319 525% 10781 Metabolite-2469 61 0.007 0.0204 355% 10604Metabolite-2370 61 0.0089 0.0239 213% 10401 Metabolite-2058 61 0.00060.0048 203% 5597 Metabolite-1073 61 0.0178 0.0357 203% 14639Metabolite-3603 61 5.89E−05 0.001 194% 16019 Metabolite-3995 61 0.00010.0017 156% 22480 Metabolite-8987 50 1.78E−05 0.0006 150% 6130Metabolite-1208 61 0.0277 0.0454 144% 9137 Metabolite-2141 61 0.01740.0353 127% 21418 Isobar-56^([1]) 61 0.0015 0.0072 122% 18015Metabolite-3113 61 0.0015 0.0072 122% 22414 Metabolite-8933 61 0.09030.0028 108% 6122 Metabolite-1206[2] 61 0.0414 0.058 104% 9024Metabolite-2111 61 0.0241 0.0432 104% 18073 Metabolite-5270 61 0.01590.0337 92% 12711 Metabolite-3053 61 0.0574 0.0752 89% 13512Metabolite-3315 61 0.028 0.0454 89% 16471 Metabolite-4238 61 0.0460.0627 89% 5618 Metabolite-1085[3] 61 0.0371 0.0558 85% 7654Metabolite-1836 61 0.002 0.0085 82% 16860 Metabolite-4517 50 0.00270.011 79% 14715 Metabolite-3653[4] 61 0.0037 0.0129 72% 21410Isobar-52^([5]) 61 0.0018 0.008 69% 17885 Metabolite-5147 61 0.01550.0332 56% 22494 Metabolite-8994 50 0.0003 0.003 56% 10850Metabolite-2548[6] 61 0.0668 0.0819 52% 15213 Metabolite-3808 615.92E−06 0.0004 52% 21415 Metabolite-8209 50 0.011 0.0255 52% 6266Metabolite-1286 61 9.64E−05 0.0013 49% 7127 Metabolite-1616 61 0.00190.0084 49% 7272 Metabolite-1679 61 0.0562 0.0746 45% 8509Metabolite-2041 61 0.0104 0.0255 45% 9313 Metabolite-2172 61 0.02460.0432 45% 9905 Metabolite-2231 61 0.0167 0.0343 43% 16071Metabolite-4020 50 0.002 0.0085 41% 22441 Metabolite-8950 61 0.07980.0925 39% 19273 Metabolite-6108 61 0.0405 0.058 37% 16233Isobar-13^([7]) 61 0.0126 0.0282 33% 9324 Metabolite-2173 61 0.05770.0752 33% 19787 Metabolite-6746 61 0.0244 0.0432 30% 20299Metabolite-7266 50 0.0212 0.0402 30% 9122 Metabolite-2137 61 0.08360.0961 28% 21404 Isobar-48^([8]) 61 0.0487 0.0658 27% 21011Metabolite-7888 50 0.0031 0.012 27% 13142 Metabolite-3165 61 0.04120.058 18% 19372 Metabolite-6269 50 0.0192 0.0374 16% 16285Metabolite-2798 50 0.045 0.062 15% 13505 Metabolite-3313 61 0.03380.0514 11% 17064 Metabolite-4624 50 0.0619 0.0779 −15% 19599Metabolite-6649 50 0.0287 0.0457 −18% 16074 Metabolite-2758 50 0.04170.058 −19% 20361 Metabolite-7327 61 0.0008 0.0056 −20% 7081Metabolite-1609 61 0.0604 0.0767 −21% 17919 Metabolite-5187 61 0.00720.0208 −22% 17978 Metabolite-5213 50 0.0689 0.0838 −23% 18273Metabolite-5420 50 0.0784 0.0916 −23% 16984 Metabolite-4599 50 0.06240.0779 −24% 11545 Metabolite-2767 61 0.0412 0.058 −25% 22509Metabolite-9011 61 0.0063 0.0191 −29% 12856 Metabolite-3123 61 0.07680.0904 −32% 16060 Metabolite-4014 50 0.0253 0.0435 −32% 16843Metabolite-4510 50 0.0289 0.0457 −34% 17960 Metabolite-5207 50 0.00030.003 −35% 16116 Metabolite-4051 50 0.0226 0.0417 −36% 14595Metabolite-3576 61 0.0106 0.0255 −36% 8176 Metabolite-1974 61 0.00080.0056 −37% 15085 Metabolite-3776 61 0.0093 0.0239 −39% 16705Metabolite-4428 61 0.0059 0.0182 −40% 19505 Metabolite-6547 61 0.02670.0446 −41% 11056 Metabolite-2568 61 0.075 0.0891 −43% 22507Metabolite-9010 50 0.0396 0.0578 −46% 9130 Metabolite-2139 61 0.0010.006 −48% 22381 Metabolite-8908 61 0.0389 0.0574 −50% 18702Metabolite-5767 61 0.0234 0.0428 −56% 11379 Metabolite-2725 61 6.71E−050.001 −69% 22501 Metabolite-9007 61 0.0007 0.0052 −79% 22534Metabolite-9016 61 2.36E−05 0.0006 −86% ^([1])Isobar-56 includesDL-pipecolic acid, 1-amino-1-cyclopentanecarboxylic acid. [2]Possiblemethyltestosterone and others. [3]Possible isolobinine or4-aminoestra-1,3,5(10)-triene-3,17beta-diol. [4]Possible stachydrine.^([5])Isobar-52 includes iminodiacetic acid, L-aspartic acid.[6]Possible Cl adduct of uric acid. ^([7])Isobar 13 includes5-keto-D-gluconic acid, 2-keto-L-gulonic acid, D-glucuronic acid,D(+)-galacturonic acid. ^([8])Isobar 48 includesSerine-2,2-amino-2-methyl-1,3-propanediol,diethanolamine.

TABLE 22 Metabolite biomarkers of dilated cardiomyopathy (DCM) inplasma. % Change COMP_ID COMPOUND LIB_ID p-value q-value in DCM TG/NTG17007 Metabolite - 4609 61 0.0003 0.0895 83% 1.83 1299 tyrosine 610.0005 0.0895 83% 1.83 20161 Metabolite - 7088 61 0.0008 0.0895 239% 3.39 19787 Metabolite - 6746 61 0.0010 0.0895 62% 1.62 20699meso-erythritol 50 0.0013 0.0895 46% 1.46 18968 Metabolite - 5919 610.0014 0.0895 100%  2.00 1107 allantoin 50 0.0017 0.0901 54% 1.54 1431(p-Hydroxyphenyl)lactic acid 61 0.0028 0.1283 114%  2.14 584 mannose 500.0034 0.1411 −41%  0.59 15632 Metabolite - 3980 61 0.0054 0.1997 64%1.64 396 glutarate 61 0.0069 0.2328 181%  2.81 11292 Metabolite - 270361 0.0080 0.2473 124%  2.24 18829 phenylalanine 61 0.0098 0.2547 39%1.39 15286 Metabolite - 3848 61 0.0103 0.2547 92% 1.92 13575Metabolite - 3324 61 0.0112 0.2547 250%  3.50 14786 Metabolite - 3697 610.0121 0.2547 52% 1.52 22597 Metabolite - 9041 61 0.0130 0.2547 −70% 0.30 15611 Metabolite - 3971 61 0.0132 0.2547 66% 1.66 11813Metabolite - 2809 61 0.0137 0.2547 −28%  0.72 6571 Metabolite - 1397 610.0139 0.2547 75% 1.75 21418 Isobar 56 includes DL- 61 0.0151 0.2547 38%1.38 pipecolic acid, 1-amino-1- cyclopentanecarboxylic acid 11299Metabolite - 2706 61 0.0159 0.2547 65% 1.65 6305 Metabolite - A-1254 610.0163 0.2547 64% 1.64 19857 Metabolite - 6783 61 0.0165 0.2547 135% 2.35 21044 (s)-2-hydroxybutyric acid 50 0.0186 0.2722 105%  2.05 16016Metabolite - 3994 61 0.0209 0.2722 92% 1.92 5440 Metabolite - A-1014 610.0221 0.2722 41% 1.41 22555 Metabolite - 9027 50 0.0223 0.2722 −53% 0.47 10737 Isobar 1 includes mannose, 61 0.0227 0.2722 −33%  0.67fructose, glucose, galactose, alpha-L-sorbopyranose, Inositol, D-allose,D-(+)- altrose, D-psicone, L-(+)- gulose, allo-inositol 20830Metabolite - 7762 61 0.0231 0.2722 48% 1.48 14961 Metabolite - 3752 610.0234 0.2722 −46%  0.54 15670 2-methylhippuric acid 61 0.0234 0.272253% 1.53 13142 Metabolite - 3165 61 0.0285 0.3038 37% 1.37 605 uracil 500.0286 0.3038 217%  3.17 21011 Metabolite - 7888 50 0.0298 0.3038 37%1.37 8959 Metabolite - 2100 61 0.0305 0.3038 −29%  0.71 20169Metabolite - 7092 61 0.0320 0.3038 88% 1.88 5776 Metabolite - A-1194 610.0322 0.3038 89% 1.89 6126 Metabolite - 1207 61 0.0326 0.3038 52% 1.5212459 Isobar 10 includes glutamine, 61 0.0327 0.3038 −31%  0.69H-beta-ala-gly-OH, 1- methylguanine, H-Gly-Sar- OH lysine 31553-ureidopropionic acid 61 0.0351 0.3093 60% 1.60 15541 Metabolite - 395761 0.0360 0.3093 43% 1.43 1303 malic acid 50 0.0388 0.3093 108%  2.0815737 hydroxyacetic acid 50 0.0392 0.3093 38% 1.38 527 lactate 50 0.03990.3093 65% 1.65 1670 urea 50 0.0410 0.3093 23% 1.23 159492′-deoxycytidine 61 0.0413 0.3093 24% 1.24 7272 Metabolite - 1679 610.0415 0.3093 251%  3.51 12011 Metabolite - 2848 61 0.0425 0.3093 53%1.53 1643 fumaric acid 50 0.0437 0.3093 70% 1.70 1574 histamine 610.0452 0.3093 74% 1.74 22566 Metabolite - 9029 61 0.0458 0.3093 −63% 0.37 15140 L-kynurenine 61 0.0458 0.3093 49% 1.49 220261-methylguanidine 50 0.0462 0.3093 19% 1.19 hydrochloride 7127Metabolite - 1616 61 0.0473 0.3093 −38%  0.62 1604 uric acid 50 0.04780.3093 146%  2.46 7429 Metabolite - 1733 61 0.0492 0.3093 37% 1.37 12626Metabolite - 3003 50 0.0500 0.3093 51% 1.51 2849 guanosine5′-monophosphate 61 0.0509 0.3093 −58%  0.42 16327 Metabolite - 4161 610.0514 0.3093 35% 1.35 14715 Metabolite - 3653 - Possible 61 0.05210.3093 29% 1.29 stachydrine 1507 palmitoleic acid 50 0.0522 0.3093 −54% 0.46 9491 Metabolite - 2185 61 0.0525 0.3093 97% 1.97 2734gamma-L-glutamyl-L- 61 0.0541 0.3112 34% 1.34 tyrosine 11235Metabolite - 2690 61 0.0548 0.3112 −34%  0.66 13775 Metabolite - 3370 610.0561 0.3112 38% 1.38 17960 Metabolite - 5207 50 0.0569 0.3112 −32% 0.68 1587 N-acetyl-L-leucine 61 0.0577 0.3112 75% 1.75 20798Metabolite - 7748 61 0.0581 0.3112 −41%  0.59 2832 adenosine5′-monophosphate 61 0.0589 0.3112 −80%  0.20 19294 Metabolite - 6134 610.0596 0.3112 31% 1.31 15278 Metabolite - 3843 61 0.0603 0.3112 −31% 0.69 15255 Metabolite - 3833 61 0.0614 0.3127 74% 1.74 16468Metabolite - 4236 61 0.0644 0.3224 −31%  0.69 10309 Metabolite - 2277 610.0651 0.3224 35% 1.35 1302 methionine 61 0.0705 0.3425 21% 1.21 17885Metabolite - 5147 61 0.0710 0.3425 76% 1.76 13038 Metabolite - 3143 610.0726 0.3440 53% 1.53 11411 Metabolite - 2746 61 0.0732 0.3440 79% 1.796373 Metabolite - A-1304 61 0.0749 0.3454 −25%  0.75 22259 Isobar 59includes N(′6)- 61 0.0756 0.3454 −29%  0.71 trimethyl-L-lysine, H-homoarg-OH 7081 Metabolite - 1609 61 0.0762 0.3454 67% 1.67 2856 uridine5′-monophosphate 61 0.0807 0.3484 −54%  0.46 16983 Metabolite - 4598 500.0813 0.3484 34% 1.34 20092 Metabolite - 7050 61 0.0818 0.3484 −22% 0.78 14439 Metabolite - 3498 61 0.0833 0.3484 26% 1.26 12682Metabolite - 3044 61 0.0841 0.3484 77% 1.77 18281 2-hydroxyhippuric acid61 0.0843 0.3484 21% 1.21 12129 beta-hydroxyisovaleric acid 50 0.08640.3484 17% 1.17 14117 Metabolite - 3441 61 0.0867 0.3484 −20%  0.8020488 D-glucose 50 0.0874 0.3484 −25%  0.75 9216 Metabolite - 2168 610.0884 0.3484 −21%  0.79 19596 Metabolite - 6647 50 0.0885 0.3484 28%1.28 16819 Metabolite - 4496 50 0.0909 0.3484 19% 1.19 22584Metabolite - 9038 61 0.0910 0.3484 −55%  0.45 21650 Metabolite - 8409 610.0910 0.3484 −38%  0.62 22598 Metabolite - 9042 61 0.0920 0.3484 −65% 0.35 54 tryptophan 61 0.0927 0.3484 19% 1.19 16655 Metabolite - 4362 500.0946 0.3484 −37%  0.63 19402 Metabolite - 6346 50 0.0969 0.3484 −21% 0.79 11661 indole-3-pyruvic acid 61 0.0972 0.3484 60% 1.60 1561alpha-tocopherol 50 0.0974 0.3484 38% 1.38 8180 Metabolite - 1975 610.0982 0.3484 35% 1.35 16666 Metabolite - 4365 50 0.0983 0.3484 −28% 0.72 2132 citrulline 50 0.0985 0.3484 51% 1.51 1508 pantothenic acid 610.1014 0.3489 37% 1.37 1572 glyceric acid 50 0.1017 0.3489 14% 1.1421732 Metabolite - 8475 61 0.1020 0.3489 −38%  0.62 9130 Metabolite -2139 61 0.1024 0.3489 105%  2.05 21654 Metabolite - 8413 61 0.10430.3522 −25%  0.75 10461 Metabolite - 2313 61 0.1057 0.3534 −27%  0.7316511 Metabolite - 4274 50 0.1072 0.3534 69% 1.69 1638 arginine 500.1075 0.3534 128%  2.28 13345 Metabolite - 3244 61 0.1087 0.3543 24%1.24 1493 ornithine 50 0.1183 0.3823 105%  2.05 5809 3-indoxyl sulfate61 0.1251 0.3991 80% 1.80 1648 serine 50 0.1261 0.3991 73% 1.73 1826folic acid 61 0.1268 0.3991 −66%  0.34 512 asparagine 50 0.1357 0.418164% 1.64 514 cytidine 61 0.1358 0.4181 69% 1.69 16244 Isobar 21 includesgamma- 61 0.1365 0.4181 −31%  0.69 aminobutyryl-L-histidine, L- anserine1598 N-tigloylglycine 61 0.1373 0.4181 16% 1.16 17091 Metabolite - 464161 0.1396 0.4217 40% 1.40 17665 p-hydroxybenzaldehyde 61 0.1424 0.424218% 1.18 1494 5-oxoproline 50 0.1430 0.4242 24% 1.24 8336 Metabolite -2005 61 0.1439 0.4242 82% 1.82 13214 Metabolite - 3183 - possible 610.1516 0.4351 28% 1.28 gamma-L-glutamyl-L- phenylalanine 1336n-hexadecanoic acid 50 0.1518 0.4351 −14%  0.86 21701 Metabolite - 845461 0.1537 0.4351 −34%  0.66 10141 Metabolite - A-2035 61 0.1540 0.4351−15%  0.85 17028 Metabolite - 4611 50 0.1542 0.4351 19% 1.19 1432alphahydroxybenzeneacetic 61 0.1546 0.4351 −27%  0.73 acid 6771Metabolite - 1460 61 0.1581 0.4415 −25%  0.75 12774 Metabolite - 3094 500.1632 0.4513 −13%  0.87 20084 Metabolite - 7047 61 0.1656 0.4513 17%1.17 1651 pyridoxal 61 0.1692 0.4513 24% 1.24 12924 Metabolite - 3131 610.1702 0.4513 20% 1.20 8072 Metabolite - 1958 61 0.1703 0.4513 17% 1.171126 alanine 50 0.1714 0.4513 61% 1.61 22567 Metabolite - 9030 61 0.17210.4513 −51%  0.49 6413 Metabolite - 1342 - possible 61 0.1724 0.4513 29%1.29 phenylacetylglutamine or formyl-N-acetyl-5- methoxykynurenamine9137 Metabolite - 2141 61 0.1725 0.4513 23% 1.23 18232 Metabolite - 540350 0.1746 0.4537 16% 1.16 19372 Metabolite - 6269 50 0.1776 0.4565 19%1.19 542 3-hydroxybutanoic acid 50 0.1785 0.4565 50% 1.50 11323Metabolite - 2711 61 0.1806 0.4565 72% 1.72 606 uridine 61 0.1806 0.456593% 1.93 21631 Metabolite - 8403 50 0.1955 0.4908 24% 1.24 15118Metabolite - 3784 61 0.2010 0.4996 22% 1.22 22572 Metabolite - 9034 500.2035 0.4996 28% 1.28 15121 Metabolite - 3786 61 0.2050 0.4996 −53% 0.47 20950 Metabolite - 7846 50 0.2054 0.4996 −19%  0.81 1649 valine 500.2076 0.4996 55% 1.55 1284 threonine 50 0.2098 0.4996 61% 1.61 16992Metabolite - 4603 61 0.2133 0.4996 38% 1.38 7650 Metabolite - 1834 610.2138 0.4996 −19%  0.81 14753 Metabolite - 3663 61 0.2148 0.4996 19%1.19 17627 Metabolite - 4986 50 0.2153 0.4996 41% 1.41 19919Metabolite - 6832 61 0.2160 0.4996 −34%  0.66 18969 Metabolite - 5920 610.2161 0.4996 46% 1.46 22320 Metabolite - 8889 50 0.2165 0.4996 38% 1.3812907 cGMP 61 0.2214 0.5078 27% 1.27 14759 Metabolite - 3667 61 0.22650.5163 10% 1.10 6379 Metabolite - 1329 61 0.2343 0.5308 28% 1.28 15872malonic acid 61 0.2395 0.5345 46% 1.46 10825 Metabolite - 2546 61 0.24060.5345 −7% 0.93 14988 Metabolite - 3756 61 0.2407 0.5345 −10%  0.9015990 L-alpha- 61 0.2436 0.5345 −35%  0.65 glycerophosphorylcholine12780 Metabolite - 3098 50 0.2443 0.5345 72% 1.72 9002 Metabolite - 210761 0.2446 0.5345 −33%  0.67 16044 Metabolite - 4005 50 0.2553 0.5546 58%1.58 60 leucine 50 0.2579 0.5570 47% 1.47 1827 riboflavine 61 0.26090.5573 44% 1.44 22032 Metabolite - 8766 50 0.2610 0.5573 −13%  0.8716070 Metabolite - 4019 50 0.2653 0.5613 −15%  0.85 1301 lysine 500.2661 0.5613 72% 1.72 6253 Metabolite - 1283 61 0.2689 0.5613 58% 1.5814043 Metabolite - 3428 61 0.2689 0.5613 −26%  0.74 19513 Metabolite -6552 61 0.2776 0.5731 −31%  0.69 1708 7,8-dihydrofolic acid 61 0.27910.5731 −32%  0.68 8649 Metabolite - 2053 61 0.2796 0.5731 −15%  0.8515753 hippuric acid 61 0.2839 0.5731 29% 1.29 16232 Isobar 17 includesarginine, 61 0.2839 0.5731 21% 1.21 N-alpha-acetyl-ornithine 1591N-acetyl-L-valine 61 0.2857 0.5731 42% 1.42 19374 Metabolite - 6270 500.2867 0.5731 −20%  0.80 15122 glycerol 50 0.2870 0.5731 26% 1.26 15412Metabolite - 3910 61 0.2889 0.5739 −34%  0.66 2342 serotonin 61 0.29200.5770 −54%  0.46 13512 Metabolite - 3315 61 0.2938 0.5776 22% 1.2222590 Metabolite - 9040 61 0.2957 0.5782 −55%  0.45 12789 Metabolite -3107 50 0.3012 0.5859 39% 1.39 16138 Metabolite - 4080 50 0.3045 0.5893−18%  0.82 15681 4-Guanidinobutanoic acid 61 0.3070 0.5908 36% 1.36 1125isoleucine 50 0.3094 0.5908 43% 1.43 14502 Metabolite - 3539 61 0.31090.5908 46% 1.46 14406 Metabolite - 3493 61 0.3119 0.5908 −16%  0.84 1898proline 61 0.3133 0.5908 10% 1.10 2129 oxitryptan 61 0.3172 0.5952 −17% 0.83 15125 (2-Aminoethyl)phosphonate 61 0.3212 0.5961 −23%  0.77 16226Isobar 28 includes L- 61 0.3213 0.5961 −15%  0.85 threonine,L-allothreonine, L- homoserine, (S)-(−)-4-amino 2-hydroxybutyric acid6104 tryptamine 50 0.3225 0.5961 26% 1.26 12719 Metabolite - 3055 -possible 61 0.3251 0.5980 32% 1.32 NH3 adduct of hippuric acid 15113Metabolite - 3783 61 0.3303 0.6045 −17%  0.83 16821 Metabolite - 4498 500.3358 0.6098 11% 1.11 15743 N,N-dimethylarginine 61 0.3377 0.6098 72%1.72 12782 Metabolite - 3100 50 0.3397 0.6098 57% 1.57 22570Metabolite - 9033 50 0.3430 0.6098 −24%  0.76 10147 Metabolite - A-203661 0.3459 0.6098 −12%  0.88 21046 digalacturonic acid 61 0.3472 0.6098−25%  0.75 12773 Metabolite - 3093 50 0.3483 0.6098 24% 1.24 183305-methyltetrahydrofolic acid 61 0.3499 0.6098 −21%  0.79 20035Metabolite - 7008 61 0.3521 0.6098 −13%  0.87 22154 bradykinin 61 0.35300.6098 −45%  0.55 22133 DL-hexanoyl-carnitine 61 0.3547 0.6098 50% 1.505628 Metabolite - 1086 61 0.3567 0.6098 −33%  0.67 10743 Isobar 4includes Gluconic 61 0.3597 0.6098 13% 1.13 acid, DL-arabinose,D-ribose, L-xylose, DL-lyxose, D- xylulose, galactonic acid 16985Metabolite - 4600 61 0.3606 0.6098 32% 1.32 1476 glucarate 50 0.36090.6098 18% 1.18 17860 Metabolite - 5127 61 0.3632 0.6098 28% 1.28 5466Metabolite - A-1030 61 0.3635 0.6098 23% 1.23 17845 Metabolite - 5108 610.3684 0.6098 17% 1.17 20308 Metabolite - 7270 61 0.3690 0.6098 19% 1.1918761 Metabolite - 5793 61 0.3694 0.6098 57% 1.57 10476 Metabolite -2316 61 0.3710 0.6098 −15%  0.85 15382 Metabolite - 3898 61 0.37220.6098 17% 1.17 18467 cis-5,8,11,14,17- 61 0.3728 0.6098 32% 1.32eicosapentaenoic acid 15529 Metabolite - 3951 61 0.3733 0.6098 14% 1.1415336 tartaric acid 61 0.3743 0.6098 16% 1.16 21012 Metabolite - 7889 500.3786 0.6098 16% 1.16 12533 Metabolite - 2915 50 0.3809 0.6098 −14% 0.86 10700 Metabolite - 2393 61 0.3831 0.6098 26% 1.26 12753Metabolite - 3074 50 0.3842 0.6098 15% 1.15 10357 Metabolite - A-2055 610.3856 0.6098 −24%  0.76 21763 Metabolite - 8507 61 0.3862 0.6098 −10% 0.90 15074 Metabolite - 3774 61 0.3893 0.6098 18% 1.18 10286Metabolite - 2272 61 0.3909 0.6098 47% 1.47 14480 Metabolite - 3521 610.3909 0.6098 −42%  0.58 13872 Metabolite - 3393 61 0.3933 0.6098 52%1.52 8300 Metabolite - 1988 61 0.3934 0.6098 −13%  0.87 22586Metabolite - 9039 61 0.3939 0.6098 −48%  0.52 13920 Metabolite - 3404 610.3967 0.6107 16% 1.16 12645 Metabolite - 3017 50 0.3978 0.6107 24% 1.2418369 gamma-glu-leu 61 0.3997 0.6111 11% 1.11 19368 Metabolite - 6267 500.4016 0.6115 29% 1.29 19613 Metabolite - 6670 61 0.4043 0.6131 −20% 0.80 12756 Metabolite - 3077 50 0.4130 0.6237 25% 1.25 7644 Metabolite -1831 61 0.4185 0.6289 18% 1.18 12639 Metabolite - 3012 50 0.4198 0.628946% 1.46 6130 Metabolite - 1208 61 0.4239 0.6324 −41%  0.59 11053Metabolite - 2567 61 0.4256 0.6324 10% 1.10 17390 Metabolite - 4806 500.4286 0.6335 −7% 0.93 14247 Metabolite - 3475 61 0.4319 0.6335 14% 1.1420234 Metabolite - 7170 61 0.4326 0.6335 25% 1.25 17327 Metabolite -4767 50 0.4364 0.6335 13% 1.13 17359 Metabolite - 4791 50 0.4383 0.6335−21%  0.79 22163 EDTA 50 0.4387 0.6335 47% 1.47 8176 Metabolite - 197461 0.4407 0.6335 −16%  0.84 1647 glutamine 50 0.4446 0.6335 44% 1.4417614 Metabolite - 4966 50 0.4467 0.6335 −29%  0.71 5430 Metabolite -A-1008 61 0.4497 0.6335 −5% 0.95 19462 Metabolite - 6446 50 0.45090.6335 −9% 0.91 6398 Metabolite - 1335 61 0.4510 0.6335 −19%  0.81 15639Metabolite - 3984 61 0.4520 0.6335 54% 1.54 12777 Metabolite - 3097 500.4558 0.6335 25% 1.25 15805 maltose 50 0.4590 0.6335 25% 1.25 1366trans-4-hydroxyproline 50 0.4603 0.6335 31% 1.31 4966 xylitol 50 0.46120.6335 10% 1.10 1419 5′-s-methyl-5′-thioadenosine 61 0.4617 0.6335 18%1.18 11777 glycine 50 0.4623 0.6335 36% 1.36 22020 Metabolite - 8749 500.4627 0.6335  9% 1.09 21047 3-methyl-2-oxobutyric 61 0.4640 0.6335−12%  0.88 16290 Metabolite - 4133 50 0.4655 0.6335 −7% 0.93 12803Metabolite - A-2441 50 0.4671 0.6335 −12%  0.88 8527 Metabolite - A-193161 0.4700 0.6335 −3% 0.97 19013 Metabolite - 5931 61 0.4701 0.6335  7%1.07 22568 Metabolite - 9031 61 0.4758 0.6335 −43%  0.57 12783Metabolite - 3101 50 0.4760 0.6335 27% 1.27 5533 Metabolite - A-1096 610.4762 0.6335 29% 1.29 16805 Metabolite - 4488 61 0.4772 0.6335 21% 1.2122132 DL-alpha-hydroxyisocaproic 61 0.4774 0.6335 15% 1.15 acid 16071Metabolite - 4020 50 0.4818 0.6360  7% 1.07 22439 Metabolite - 8949 610.4839 0.6360 17% 1.17 5765 Metabolite - 1142 - possible 61 0.48770.6360 −13%  0.87 5-hydroxypentanoate or beta- hydroxyisovaleric acid1481 inositol 1-phosphate 50 0.4879 0.6360 −13%  0.87 13217 Metabolite -3184 61 0.4913 0.6360 15% 1.15 5689 Metabolite - 1111 - possible 610.4925 0.6360 −19%  0.81 methylnitronitrosoguanidine or ethylthiocarbamoylacetate 22166 glu-glu 61 0.4985 0.6360 29% 1.29 21127monopalmitin 50 0.4987 0.6360 −10%  0.90 22601 Metabolite - 9044 500.4990 0.6360 25% 1.25 18384 Metabolite - 5487 50 0.5030 0.6360 −12% 0.88 18943 Metabolite - 5912 61 0.5031 0.6360 −13%  0.87 22145acetyl-L-carnitine 61 0.5078 0.6360 20% 1.20 17486 Metabolite - 4886 610.5102 0.6360 −14%  0.86 14639 Metabolite - 3603 61 0.5123 0.6360 16%1.16 12785 Metabolite - 3103 50 0.5144 0.6360 38% 1.38 9016 Metabolite -2109 61 0.5147 0.6360 −6% 0.94 10136 Metabolite - A-2034 61 0.51600.6360  4% 1.04 1417 kynurenic acid 61 0.5187 0.6360 12% 1.12 16893Metabolite - 4530 61 0.5188 0.6360 107%  2.07 1564 citric acid 50 0.51890.6360 12% 1.12 22001 3-hydroxyoctanoic acid 61 0.5214 0.6360 19% 1.1918349 DL-indole-3-lactic acid 61 0.5214 0.6360 13% 1.13 16080Metabolite - 4026 61 0.5228 0.6360 −26%  0.74 151971-methylnicotinamide-1 61 0.5243 0.6360 24% 1.24 18172 Metabolite - 539161 0.5247 0.6360 −13%  0.87 2240 homogentisate 61 0.5248 0.6360 −6% 0.9415063 Metabolite - 3772 61 0.5263 0.6360 17% 1.17 5618 Metabolite -1085 - possible 61 0.5287 0.6360 −8% 0.92 isolobinine or 4-aminoestra-1,3,5(10)-triene-3,17beta-diol 59 histidine 61 0.5290 0.6360 −8% 0.925478 Metabolite - A-1036 61 0.5339 0.6394 −7% 0.93 21013 Metabolite -7890 50 0.5363 0.6394 −15%  0.85 22262 Isobar 61 includes 4-methyl- 610.5381 0.6394 −10%  0.90 2-oxovaleric acid, 3-methyl- 2-oxovaleric acid10850 Metabolite - 2548 - possible 61 0.5398 0.6394 21% 1.21 Cl adductof uric acid 22360 Metabolite - 8901 61 0.5414 0.6394 16% 1.16 5475Metabolite - A-1033 61 0.5423 0.6394 12% 1.12 8509 Metabolite - 2041 610.5445 0.6394  7% 1.07 20694 oxalic acid 61 0.5456 0.6394 −7% 0.93 18010Metabolite - 5231 61 0.5487 0.6411 33% 1.33 2183 thymidine 61 0.55160.6423 10% 1.10 2150 pyridoxamine 61 0.5544 0.6432 −4% 0.96 19708Metabolite - 6711 61 0.5558 0.6432 −9% 0.91 22130 DL-3-phenyllactic acid61 0.5598 0.6457 −16%  0.84 16711 Metabolite - 4431 61 0.5614 0.6457−15%  0.85 20391 Metabolite - 7334 61 0.5785 0.6615 −13%  0.87 16189Metabolite - 4097 61 0.5787 0.6615  9% 1.09 7177 Metabolite - 1656 610.5817 0.6623 36% 1.36 10655 Metabolite - 2388 61 0.5830 0.6623 11% 1.1116506 Metabolite - 4271 50 0.5872 0.6651 26% 1.26 18146 Metabolite -5366 50 0.5890 0.6651 −14%  0.86 19494 Metabolite - 6506 50 0.60320.6752 −11%  0.89 18109 isocitrate 61 0.6041 0.6752 20% 1.20 1110arachidonic acid 50 0.6062 0.6752  8% 1.08 17283 Metabolite - 4750 610.6064 0.6752 −15%  0.85 13211 Metabolite - 3182 61 0.6084 0.6752 −13% 0.87 12726 Metabolite - 3058 50 0.6104 0.6752 25% 1.25 15996 aspartate50 0.6134 0.6752 27% 1.27 16705 Metabolite - 4428 61 0.6135 0.6752 13%1.13 16865 Metabolite - 4522 50 0.6143 0.6752  6% 1.06 12648Metabolite - 3020 50 0.6166 0.6755 −6% 0.94 6266 Metabolite - 1286 610.6183 0.6755  6% 1.06 1105 Linoleic acid 50 0.6208 0.6755 −6% 0.9415730 suberic acid 61 0.6219 0.6755 19% 1.19 12650 Metabolite - 3022 500.6242 0.6761 24% 1.24 20031 Metabolite - 7007 61 0.6307 0.6811 −10% 0.90 16860 Metabolite - 4517 50 0.6346 0.6815 −9% 0.91 17064Metabolite - 4624 50 0.6349 0.6815 19% 1.19 19934 inositol 50 0.63650.6815 11% 1.11 12663 Metabolite - 3030 50 0.6414 0.6836 29% 1.29 20866Metabolite - 7786 61 0.6422 0.6836  4% 1.04 22602 Metabolite - 9045 500.6447 0.6843 −13%  0.87 12593 Metabolite - 2973 50 0.6526 0.6907  3%1.03 15500 carnitine 61 0.6619 0.6957 −5% 0.95 7595 Metabolite - 1817 610.6633 0.6957  5% 1.05 19364 Metabolite - 6246 50 0.6655 0.6957 13% 1.1320248 Metabolite - 7177 61 0.6667 0.6957 10% 1.10 14054 Metabolite -3430 - possible 61 0.6670 0.6957  4% 1.04 gly-leu, acetyl-lys, ala-val11438 phosphate 50 0.6686 0.6957  5% 1.05 19511 Metabolite - 6551 610.6777 0.7032  6% 1.06 20192 Metabolite - 7146 61 0.6833 0.7061 −9% 0.9116949 Metabolite - 4592 61 0.6842 0.7061 −7% 0.93 2137 biliverdin 610.6893 0.7093 −18%  0.82 19961 Metabolite - 6913 50 0.6913 0.7093  2%1.02 7107 Metabolite - A-1664 61 0.6945 0.7093 −22%  0.78 18756Metabolite - 5791 61 0.6950 0.7093 −8% 0.92 12768 Metabolite - 3088 500.7007 0.7125 14% 1.14 19362 Metabolite - 6226 50 0.7030 0.7125 −10% 0.90 12791 Metabolite - 3109 50 0.7038 0.7125 18% 1.18 528alpha-keto-glutarate 61 0.7155 0.7217 17% 1.17 10746 Isobar 6 includesvaline, 61 0.7178 0.7217 10% 1.10 betaine 10304 Metabolite - 2276 610.7191 0.7217 24% 1.24 19377 Metabolite - 6272 50 0.7207 0.7217  9% 1.0918929 Metabolite - 5907 50 0.7249 0.7239 −9% 0.91 9060 Metabolite -A-1994 61 0.7284 0.7248 16% 1.16 22577 Metabolite - 9035 50 0.73230.7248 17% 1.17 1121 heptadecanoic acid 50 0.7328 0.7248  4% 1.04 14840Metabolite - 3708 61 0.7359 0.7248  5% 1.05 16712 Metabolite - 4432 610.7373 0.7248 −3% 0.97 16285 Metabolite - A-2798 50 0.7393 0.7248 −11% 0.89 1584 methyl indole-3-acetate 61 0.7401 0.7248 −13%  0.87 18868Metabolite - 5847 50 0.7421 0.7248 13% 1.13 16843 Metabolite - 4510 500.7452 0.7248 15% 1.15 19800 Metabolite - 6750 61 0.7544 0.7248 24% 1.2412457 Metabolite - 2892 61 0.7565 0.7248 −9% 0.91 156773-methyl-L-histidine 61 0.7568 0.7248 −4% 0.96 57 glutamic acid 500.7577 0.7248 18% 1.18 21762 Metabolite - 8506 61 0.7591 0.7248  7% 1.0712770 Metabolite - 3090 50 0.7605 0.7248  7% 1.07 19367 Metabolite -6266 50 0.7620 0.7248 −6% 0.94 11770 Metabolite - 2806 61 0.7646 0.7248 5% 1.05 10148 Metabolite - 2257 61 0.7649 0.7248 13% 1.13 10570Metabolite - 2366 61 0.7663 0.7248 −6% 0.94 20267 Metabolite - 7187 610.7675 0.7248 14% 1.14 577 fructose 50 0.7684 0.7248 −7% 0.93 16308Metabolite - 4147 50 0.7687 0.7248  7% 1.07 6362 Metabolite - 1323 -possible 61 0.7707 0.7249 22% 1.22 p-cresol sulfate 12112 Metabolite -A-2314 61 0.7740 0.7261  6% 1.06 12647 Metabolite - 3019 50 0.77670.7268 18% 1.18 13104 Metabolite - 3160 61 0.7839 0.7295 −5% 0.95 17060Metabolite - 4622 61 0.7853 0.7295  7% 1.07 1365 tetradecanoic acid 500.7859 0.7295 −5% 0.95 1512 picolinic acid 50 0.7875 0.7295 −5% 0.9512673 Metabolite - 3040 50 0.7915 0.7315  7% 1.07 12876 Metabolite -3125 61 0.7960 0.7338 −8% 0.92 18892 Metabolite - 5866 61 0.8003 0.7360−5% 0.95 12035 nonanate 50 0.8032 0.7368  5% 1.05 18678 Metabolite -5730 61 0.8065 0.7371 −7% 0.93 19955 Metabolite - 6907 50 0.8075 0.7371 7% 1.07 19323 Docosahexaenoic Acid 50 0.8175 0.7428  3% 1.03 12656Metabolite - 3025 50 0.8225 0.7428 13% 1.13 12751 Metabolite - 3073 500.8227 0.7428 −10%  0.90 18706 Metabolite - 5769 61 0.8236 0.7428 −6%0.94 15064 Metabolite - 3773 61 0.8252 0.7428 10% 1.10 12666Metabolite - 3033 - possible 50 0.8278 0.7428 10% 1.10 threonine deriv-16829 Metabolite - 4503 50 0.8286 0.7428 −3% 0.97 12771 Metabolite -3091 50 0.8298 0.7428  7% 1.07 21188 1-stearoyl-rac-glycerol 50 0.84520.7498  3% 1.03 16509 Metabolite - 4273 50 0.8458 0.7498 −8% 0.92 10544Metabolite - 2329 61 0.8460 0.7498 −9% 0.91 15625 Metabolite - 3976 610.8476 0.7498  3% 1.03 6461 Metabolite - A-1329 61 0.8499 0.7498  7%1.07 17207 Metabolite - 4707 61 0.8507 0.7498  4% 1.04 12008Metabolite - 2847 61 0.8532 0.7498  6% 1.06 17389 Metabolite - 4796 500.8537 0.7498 −14%  0.86 597 phosphoenolpyruvate 61 0.8558 0.7499 −4%0.96 10111 Metabolite - A-2033 61 0.8587 0.7506 −5% 0.95 8796Metabolite - 2074 61 0.8687 0.7575  5% 1.05 7029 Metabolite - 1597 610.8755 0.7601  2% 1.02 15253 Metabolite - 3832 - possible 61 0.87570.7601  6% 1.06 phenol sulfate 20194 Metabolite - 7147 61 0.8787 0.7607−2% 0.98 15535 Metabolite - 3955 61 0.8805 0.7607  6% 1.06 15129D-alanyl-D-alanine 50 0.8826 0.7608  3% 1.03 12658 Metabolite - 3026 500.8933 0.7650  8% 1.08 10604 Metabolite - 2370 61 0.8961 0.7650 −4% 0.9613179 creatine 61 0.8973 0.7650 −4% 0.96 10781 Metabolite - 2469 610.8992 0.7650  5% 1.05 1358 octadecanoic acid 50 0.9002 0.7650  1% 1.0115365 sn-Glycerol 3-phosphate 50 0.9018 0.7650  3% 1.03 9172Metabolite - A-2000 61 0.9060 0.7650 −1% 0.99 15227 trans-aconiticacid-1 61 0.9063 0.7650  3% 1.03 5702 choline 61 0.9074 0.7650 −4% 0.9612625 Metabolite - 3002 50 0.9081 0.7650  2% 1.02 19860 Metabolite -6784 61 0.9194 0.7716  1% 1.01 12781 Metabolite - 3099 50 0.9201 0.7716 4% 1.04 13273 Metabolite - 3224 61 0.9344 0.7759  3% 1.03 21025iminodiacetic acid 50 0.9345 0.7759  2% 1.02 12912 Metabolite - 3129 610.9367 0.7759 −1% 0.99 6131 Metabolite - 1209 61 0.9387 0.7759 −3% 0.976380 Metabolite - 1330 61 0.9399 0.7759 −2% 0.98 2125 taurine 61 0.94000.7759 −3% 0.97 18118 Metabolite - 5346 50 0.9423 0.7759 −3% 0.97 19291Metabolite - 6132 61 0.9430 0.7759 −2% 0.98 12667 Metabolite - 3034 500.9440 0.7759  1% 1.01 20927 Metabolite - 7815 61 0.9519 0.7796 −1% 0.9916665 Metabolite - 4364 50 0.9529 0.7796 −2% 0.98 20228 Metabolite -7169 61 0.9548 0.7796 −2% 0.98 15600 Metabolite - 3964 61 0.9639 0.7799−1% 0.99 9905 Metabolite - 2231 61 0.9664 0.7799  0% 1.00 16496Metabolite - 4251 50 0.9670 0.7799  1% 1.01 513 creatinine 61 0.96870.7799 −1% 0.99 22581 Metabolite - 9037 61 0.9712 0.7799  2% 1.02 18882taurodeoxycholic acid 61 0.9715 0.7799  2% 1.02 12754 Metabolite - 307550 0.9801 0.7799  1% 1.01 13018 Metabolite - 3138 61 0.9829 0.7799  1%1.01 8196 Metabolite - 1979 - Cl adduct 61 0.9833 0.7799  0% 1.00 ofisobar 19 19397 Metabolite - 6326 50 0.9846 0.7799 −1% 0.99 13288Metabolite - 3228 61 0.9860 0.7799  0% 1.00 19490 Metabolite - 6488 500.9863 0.7799  1% 1.01 12757 Metabolite - 3078 50 0.9867 0.7799 −1% 0.9912790 Metabolite - 3108 50 0.9880 0.7799  1% 1.01 16512 Metabolite -4275 50 0.9908 0.7799  0% 1.00 17330 Metabolite - 4769 50 0.9910 0.7799 0% 1.00 19363 Metabolite - 6227 50 0.9929 0.7799  0% 1.00 63cholesterol 50 0.9929 0.7799  0% 1.00 10782 Metabolite - 2486 61 0.99670.7812  0% 1.00 22548 Metabolite - 9026 50 0.9988 0.7812  0% 1.00

Recursive partitioning statistical analysis of all the metabolites incardiac tissue between normal and DCM subjects identifiedMetabolite-3808 (Metabolite-3808) as a compound that separated bothgroups of subjects perfectly (LogWorth=6.61). Specifically, all subjectswith DCM phenotype had levels of Metabolite-3808 above the cutoff valueof 629275 while all the subjects with a normal phenotype hadMetabolite-3808 levels below the cutoff value (FIG. 20). The cutoffvalue in the graph in FIG. 20 is indicated by a broken line.

To evaluate the biomarkers discovered in the mouse model of DCM,analysis of human subjects was performed. Biomarkers were discovered by(1) analyzing plasma samples from different groups of human subjects todetermine the levels of metabolites in the samples and then (2)statistically analyzing the results to determine those metabolites thatwere differentially present in the two groups.

Two groups of subjects were used. One group consisted of 39 subjects (18male, 21 female) with dilated cardiomyopathy (DCM). The second groupconsisted of 31 healthy control subjects (14 male, 17 male). Subjectswere balanced for age and gender; mean age of control females was50.1+/−10.1 and DCM females was 50.0+/−11.3 while the mean age ofcontrol males was 42.7+/−11.3 and DCM males was 45.8+/−10.9.

T-tests (Table 23) were used to determine differences in the mean levelsof metabolites between the two populations (i.e., DilatedCardiomyopathy, DCM vs. Healthy control). Classification analysis wascarried out using random forest analyses to uncover the biomarkers thatcan best differentiate the two groups. The results of the Random forestanalysis are shown in Table 24 and the most important biomarkers usefulto classify subjects as healthy or DCM are listed in Table 25.

Biomarkers:

As listed below in Table 23, biomarkers were discovered that weredifferentially present between plasma samples collected from dilatedcardiomyopathy subjects and healthy subjects.

Table 23 includes, for each listed biomarker, the p-value and q-valuedetermined in the statistical analysis of the data concerning thebiomarkers and an indication of the percentage difference in the dilatedcardiomyopathy mean level as compared to the healthy mean level inplasma. “ID” refers to the compound identification number used as aprimary key for that compound in the in-house chemical database.“Library” indicates the chemical library that was used to identify thecompounds. The number 50 refers to the GC library and the numbers 200and 201 refer to the LC library. “Mouse” indicates the compounds thatwere also biomarkers discovered in the mouse model of DCM.

TABLE 23 Biomarkers for DCM % Change ID Library Compound p-value q-valuein DCM Mouse 32873 201 Metabolite - 11556 5.943E−07 0.0001 −10%  1558200 4-acetamidobutanoate 6.339E−06 0.0005  7% 32709 200 Metabolite -03056 1.925E−05 0.001 73% 33510 200 Metabolite - 12095 2.318E−05 0.001−9% 33442 200 pseudouridine 3.734E−05 0.0012  1% 32425 201dehydroisoandrosterone sulfate 0.0001 0.0012  4% (DHEA-S) 32519 200Metabolite - 11205 0.0001 0.0012 14% 32637 201 Metabolite - 11320 0.00010.0012  6% 16866 50 Metabolite - 04523 0.0001 0.0015  9% 1114 201deoxycholate 0.0001 0.0017 11% 1284 200 threonine 0.0001 0.0017  1% Yes32675 200 Metabolite - 03951 0.0001 0.0017 12% 15506 200 choline 0.00020.002 29% Yes 32652 200 Metabolite - 11335 0.0002 0.0029 −31%  32197 2013-(4-hydroxyphenyl)lactate 0.0003 0.0035 −4% Yes 33515 200 Metabolite -12100 0.0003 0.0036 31% 18929 50 Metabolite - 05907 0.0004 0.004 −3%32405 200 3-indolepropionate 0.0004 0.004 −13%  19934 50 myo-inositol0.0006 0.0051 17% Yes 33453 50 alpha-ketoglutarate 0.0006 0.0055  2%1712 200 cortisol 0.0007 0.006 −53%  32807 201 Metabolite - 11490 0.00120.0094 −12%  25607 50 Metabolite - 10437 0.0013 0.0097  4% 25459 50Metabolite - 10395 0.0015 0.0104 −3% 33477 50 erythronate* 0.0016 0.0106−25%  32635 201 Metabolite - 11318 0.0018 0.0117 17% 57 50 glutamate0.002 0.0126 −5% Yes 18254 200 paraxanthine 0.0021 0.0126 −3% 33973 201epiandrosterone sulfate 0.0021 0.0126 51% 32560 201 Metabolite - 077650.0025 0.0142 −4% 20699 50 erythritol 0.0026 0.0144 −19%  Yes 32590 201Metabolite - 11273 0.003 0.0154 −18%  32619 201 Metabolite - 11302 0.0030.0154 −11%  22602 50 Metabolite - 09045 0.0033 0.0161 −6% 32829 200Metabolite - 03653 0.0033 0.0161 −4% 32910 201 Metabolite - 11593 0.00350.0168 −8% 32599 201 Metabolite - 11282 0.0037 0.017  3% 16653 50Metabolite - 04361 0.004 0.0176  2% 18232 50 Metabolite - 05403 0.0040.0176 30% 15677 201 3-methylhistidine 0.0048 0.0206 120%  Yes 12770 50Metabolite - 03090 0.0055 0.0225 13% 31591 201 androsterone sulfate0.0055 0.0225 −78%  584 50 mannose 0.0063 0.0248 16% Yes 15140 200kynurenine 0.0067 0.0258 22% Yes 33206 201 Metabolite - 11861 0.00680.0258 −17%  33144 200 Metabolite - 11799 0.007 0.026 −13%  33507 200Metabolite - 12092 0.0076 0.0278 −3% 32740 201 Metabolite - 11423 0.00780.0278 −27%  33652 201 Metabolite - 12230 0.008 0.0279 −3% 53 200glutamine 0.0082 0.028  2% Yes 32808 201 Metabolite - 11491 0.00870.0289 39% 33516 200 Metabolite - 12101 0.0089 0.0289 80% 3147 50xanthine 0.009 0.0289 20% 12753 50 Metabolite - 03074 0.0099 0.0309 −5%1303 50 malate 0.0101 0.0309  3% Yes 32762 201 Metabolite - 11445 0.01010.0309  4% 2734 200 gamma-glutamyltyrosine 0.0115 0.0339 −4% Yes 32718200 Metabolite - 01342 0.0116 0.0339 26% 32644 200 Metabolite - 113270.0117 0.0339 −25%  32654 200 Metabolite - 11337 0.012 0.0341  9% 33937201 alpha-hydroxyisovalerate 0.0127 0.0355 38% 12017 2003-methoxytyrosine 0.0134 0.0369  7% 32753 201 Metabolite - 09789 0.01540.0418 −9% 32587 201 Metabolite - 02249 0.016 0.0427 44% 59 201histidine 0.0179 0.047 −1% Yes 22548 50 Metabolite - 09026 0.0188 0.048628% 16959 50 Metabolite - 04595 0.0191 0.0488 21% 33422 200gammaglutamylphenylalanine 0.0197 0.0494 30% 64 200 phenylalanine 0.02110.0522 −4% Yes 32110 50 Metabolite - 11086 0.0227 0.055  8% 33132 200Metabolite - 11787 0.0229 0.055 132%  32859 200 Metabolite - 115420.0236 0.0557 −19%  32672 200 Metabolite - 02546 0.024 0.0557 −5% 2104450 2-hydroxybutyrate (AHB) 0.0244 0.0557  3% Yes 1356 201 nonadecanoate(19:0) 0.025 0.0557 −1% 32198 200 acetylcarnitine 0.025 0.0557 −44%  Yes32830 200 Metabolite - 11513 0.0251 0.0557  1% 599 50 pyruvate 0.02610.0571 −7% 1358 201 stearate (18:0) 0.0269 0.0576 −13%  Yes 32701 200urate* 0.027 0.0576 −2% Yes 32393 200 glutamylvaline 0.0276 0.0576 11%24077 50 Metabolite - 09727 0.0277 0.0576 −12%  24076 50 Metabolite -09726 0.0285 0.0586 −31%  1299 200 tyrosine 0.0288 0.0587 −3% Yes 27718200 creatine 0.0294 0.059 −29%  Yes 12757 50 Metabolite - 03078 0.02990.0594 19% 1769 200 cortisone 0.0308 0.0604 33% 32836 200 HWESASXX*0.0315 0.0612 11% 33028 200 Metabolite - 01497 0.0325 0.0624 −8% 33961200 1-stearoylglycerophosphocholine 0.0329 0.0624  4% 33157 200Metabolite - 11812 0.0334 0.0624 27% 32606 201 bilirubin* 0.0336 0.0624 2% 33939 201 N-acetylthreonine 0.0358 0.0659  7% 25609 50 Metabolite -10439 0.0363 0.066 75% 19368 50 Metabolite - 06267 0.0367 0.066 −7%12789 50 Metabolite - 03107 0.0384 0.0683 17% Yes 33441 200isobutyrylcarnitine 0.0391 0.0684 −13%  34035 201 linolenate [alpha orgamma; (18:3(n-3 0.0394 0.0684 −2% or 6))] 33363 200gamma-glutamylmethionine* 0.0396 0.0684 −17%  30257 50 Metabolite -10729 0.0417 0.0712  3% 33821 200 Metabolite - 12393 0.0431 0.0724  0%33389 201 Metabolite - 12038 0.0432 0.0724  3% 21047 2013-methyl-2-oxobutyrate 0.0464 0.077 15% Yes 18349 200 indolelactate0.0472 0.0775 −55%  Yes 12110 200 isocitrate 0.0478 0.0775 11% 33405 200Metabolite - 12053 0.0481 0.0775  0% 32497 201 10c-undecenoate 0.04960.0792 17% 33738 201 Metabolite - 12316 0.0502 0.0794 −14%  19402 50Metabolite - 06346 0.0518 0.0812 53% 33964 200 [H]HWESASLLR[OH] 0.05230.0813 26% 12795 50 Metabolite - 03113 0.0535 0.0824  6% 32754 201Metabolite - 11437 0.0542 0.0827 −13%  18497 201 taurocholate 0.0550.0831 −9% 1508 200 pantothenate 0.0565 0.0847 25% Yes 32625 201Metabolite - 11308 0.0609 0.0904 −50%  32729 200 Metabolite - 114120.0621 0.0915 −26%  31555 201 pyridoxate 0.0648 0.0946 18% 33960 2001-oleoylglycerophosphocholine 0.0664 0.0961  9% 1642 201 caprate (10:0)0.068 0.0976 −5% 31454 50 cystine 0.0697 0.0985 −11%  18477 200glycodeoxycholate 0.07 0.0985 −44%  32850 201 Metabolite - 11533 0.07110.0985 155%  22895 50 Metabolite - 09299 0.0717 0.0985 −15%  31618 50Metabolite - 10964 0.0718 0.0985 −25%  34007 50 Metabolite - 12502 0.0720.0985 −12%  1638 200 arginine 0.0748 0.1014 −14%  Yes 33852 200Metabolite - 12424 0.0767 0.1031 −20%  33420 50 gamma-tocopherol* 0.07980.1058 −17%  32398 200 sebacate 0.0799 0.1058 −9% 33403 200 Metabolite -12051 0.082 0.1077 −10%  33957 200 1-heptadecanoylglycerophosphocholine0.0838 0.1085 −1% 32518 200 Metabolite - 11204 0.0849 0.1091 27% 1361201 pentadecanoate (15:0) 0.0887 0.1131 69% 1645 201 laurate (12:0)0.0906 0.1141 10% 32620 201 Metabolite - 11303 0.0908 0.1141  3% 15990200 glycerophosphorylcholine (GPC) 0.0928 0.1158 −9% Yes 27531 201hyodeoxycholate 0.0956 0.1184  2% 1105 201 linoleate (18:2(n-6)) 0.09690.1184 66% Yes 33140 200 Metabolite - 11795 0.097 0.1184 −1% 16308 50Metabolite - 04147 0.099 0.1193 38% 33927 200 Metabolite - 12481 0.09940.1193 18% 19363 50 Metabolite - 06227 0.0998 0.1193 16% 31509 50Metabolite - 10931 0.1008 0.1196 31% 32561 201 Metabolite - 11244 0.10520.1239 −11%  32846 201 Metabolite - 11529 0.1091 0.1257 253%  21630 50Metabolite - 08402 0.1098 0.1257  2% 32550 201 Metabolite - 02272 0.11020.1257 21% 1107 50 allantoin 0.1104 0.1257 −13%  Yes 32867 201Metabolite - 11550 0.1104 0.1257  5% 32549 201 Metabolite - 02269 0.11570.1309  8% 32786 200 Metabolite - 11469 0.1181 0.1321  8% 32501 201dihomo-alpha-linolenate (20:3(n-3)) 0.1188 0.1321 24% 21128 50octadecanol 0.1191 0.1321 11% 16819 50 Metabolite - 04496 0.12 0.1323−7% 33209 201 Metabolite - 11864 0.121 0.1324 −21%  32778 200Metabolite - 11461 0.1216 0.1324 −6% 32839 201 Metabolite - 11522 0.12840.1389 20% 32868 201 glycocholate* 0.1304 0.1401 −28%  33969 201stearidonate (18:4(n-3)) 0.1388 0.146 89% 12783 50 Metabolite - 031010.1405 0.146 21% 31453 50 cysteine 0.141 0.146 16% 33103 50 Metabolite -11758 0.1417 0.146 26% 32758 201 Metabolite - 11441 0.1421 0.146 24%33935 200 piperine 0.1422 0.146 −26%  33472 200 Metabolite - 120850.1426 0.146  3% 32978 200 Metabolite - 11656 0.1427 0.146 −26%  32504201 n-3 DPA (22:5(n-3)) 0.1448 0.1463 −3% 32877 201 Metabolite - 115600.1463 0.1463 47% 27273 50 Metabolite - 10506 0.1465 0.1463 36% 19370 50Metabolite - 06268 0.149 0.1472  2% 1572 50 glycerate 0.1506 0.1472  1%Yes 32346 201 glycochenodeoxycholate 0.1506 0.1472 13% 32769 201Metabolite - 11452 0.1513 0.1472 −17%  11777 50 glycine 0.1564 0.1508−3% Yes 32759 201 Metabolite - 11442 0.1572 0.1508 79% 513 200creatinine 0.161 0.1537 −2% Yes 32452 200 propionylcarnitine 0.1630.1547  9% 20675 201 1,5-anhydroglucitol (1,5-AG) 0.1674 0.1573  5%22600 50 Metabolite - 09043 0.1677 0.1573 56% 33380 201 Metabolite -12029 0.1692 0.1573 −5% 25532 50 Metabolite - 10413 0.1695 0.1573 −7%15335 50 mannitol 0.173 0.1598  2% 32952 201 Metabolite - 02277 0.17470.1605 −13%  27275 50 Metabolite - 10507 0.1777 0.1617 −8% 25522 50Metabolite - 10407 0.178 0.1617 37% 18335 50 quinate 0.1831 0.1648 17%1670 50 urea 0.1833 0.1648 −2% Yes 31266 50 fructose 0.1853 0.1652 11%32401 200 trigonelline (N′-methylnicotinate) 0.1856 0.1652 30% 33228 200Metabolite - 11883 0.1941 0.1718 −4% 32776 200 Metabolite - 11459 0.19970.1754 −13%  1121 201 margarate (17:0) 0.2008 0.1754 12% Yes 33955 2001-palmitoylglycerophosphocholine 0.2012 0.1754 −25%  11438 50 phosphate0.2028 0.1759 −7% Yes 32756 201 Metabolite - 02276 0.205 0.1769 −23% 21127 50 1-palmitoylglycerol (1-monopalmitin) 0.212 0.182 −40%  Yes 1359201 oleate(18:1(n-9)) 0.2134 0.1823 −20%  16665 50 Metabolite - 043640.2221 0.1888 63% 33662 200 Metabolite - 12240 0.2242 0.1896 17% 32572200 Metabolite - 11255 0.2263 0.1899 12% 32814 201 Metabolite - 114970.2274 0.1899 16% 12774 50 Metabolite - 03094 0.2288 0.1899 −11%  33774201 Metabolite - 12349 0.2289 0.1899 33% 33386 50 Metabolite - 120350.233 0.1905 10% 33415 201 Metabolite - 12063 0.2331 0.1905 21% 33846200 indoleacetate* 0.2365 0.1909 −16%  21049 50 1,6-anhydroglucose0.2368 0.1909 −27%  16650 50 Metabolite - 04360 0.2369 0.1909 −40% 15365 50 glycerol 3-phosphate (G3P) 0.2384 0.1913 12% Yes 22189 200palmitoylcarnitine 0.2412 0.1926 22% 30821 50 Metabolite - 10812 0.24250.1927 −47%  33620 200 Metabolite - 12199 0.2445 0.1934 16% 12129 200beta-hydroxyisovalerate 0.2499 0.1967 −3% Yes 33408 200 Metabolite -12056 0.2568 0.1992  7% 31373 50 Metabolite - 10878 0.2571 0.1992 −10% 32792 201 Metabolite - 11475 0.2573 0.1992 −17%  18392 200 theobromine0.2577 0.1992 −10%  19323 201 docosahexaenoate (DHA; 22:6(n-3)) 0.26660.2052 −14%  Yes 18394 201 theophylline 0.2846 0.218 −6% 32795 201Metabolite - 11478 0.2895 0.2208 −10%  32698 200 Metabolite - 113810.2935 0.2219 −26%  32412 200 butyrylcarnitine 0.2945 0.2219  0% 32800201 Metabolite - 11483 0.296 0.2219  0% 33198 201 Metabolite - 118530.2979 0.2219 −50%  33254 201 Metabolite - 11909 0.2988 0.2219 −26% 22842 200 cholate 0.2993 0.2219 38% 33390 201 Metabolite - 12039 0.30010.2219 −22%  12626 50 Metabolite - 03003 0.3038 0.2237 13% 12261 201taurodeoxycholic acid 0.3087 0.2264 −46%  32578 200 Metabolite - 112610.3152 0.2301 −18%  18868 50 Metabolite - 05847 0.3218 0.2328 −1% 32735200 Metabolite - 01911 0.3229 0.2328 −12%  27719 50 galactonic acid0.3253 0.233 −14%  15122 50 glycerol 0.3269 0.233 16% Yes 33204 201Metabolite - 11859 0.3274 0.233  3% 32328 200 hexanoylcarnitine 0.32940.2334 −17%  1898 200 proline 0.3309 0.2334 −41%  Yes 21421 50Metabolite - 08214 0.3322 0.2334  2% 32813 201 Metabolite - 11496 0.3350.2334 −4% 32697 200 Metabolite - 11380 0.3352 0.2334 74% 22320 50Metabolite - 08889 0.3383 0.2334 48% 32634 201 Metabolite - 11317 0.33870.2334 41% 33194 201 Metabolite - 11849 0.3388 0.2334 −34%  527 50lactate 0.3402 0.2335 95% Yes 33154 200 Metabolite - 11809 0.3458 0.2356−18%  32492 201 caprylate (8:0) 0.3467 0.2356  4% 32838 200 Metabolite -11521 0.3511 0.2371 −6% 32616 201 Metabolite - 11299 0.3571 0.2402 −26% 22154 200 bradykinin 0.3614 0.2422 −4% Yes 32875 200 Metabolite - 115580.3753 0.2495 −33%  32971 200 Metabolite - 11654 0.3753 0.2495 −11% 16634 50 Metabolite - 04357 0.3868 0.2551  2% 19576 50 Metabolite -06627 0.3898 0.2562 −1% 33570 200 Metabolite - 12154 0.3919 0.2565 99%2137 200 biliverdin 0.3952 0.2577 27% Yes 32854 200 Metabolite - 115370.4062 0.2638 −3% 17747 200 sphingosine 0.4125 0.2669 −20%  1365 201myristate (14:0) 0.4169 0.2683 66% Yes 32511 201 EDTA* 0.4192 0.2683 63%32767 201 Metabolite - 11450 0.4192 0.2683  7% 32847 201 Metabolite -11530 0.4303 0.2743 10% 17805 201 dihomolinolenate (20:2(n-6)) 0.43310.2743 15% 32793 200 Metabolite - 11476 0.4346 0.2743 88% 12781 50Metabolite - 03099 0.4351 0.2743 64% 1648 50 serine 0.4384 0.2753 −10% Yes 32557 201 Metabolite - 06126 0.4398 0.2753 −3% 1301 50 lysine 0.44380.2767 24% Yes 1126 50 alanine 0.4497 0.2786  2% Yes 569 200 caffeine0.4501 0.2786 25% 32732 201 Metabolite - 11415 0.4532 0.2788 −2% 3308950 Metabolite - 11744 0.4537 0.2788 64% 21184 200 oleoylglycerol(monoolein) 0.4564 0.2795 150%  22481 50 Metabolite - 08988 0.46480.2828 −24%  18369 200 gamma-glutamylleucine 0.4651 0.2828 −9% Yes 2029950 Metabolite - 07266 0.4717 0.2858 14% 33882 201 Metabolite - 124400.4802 0.2895 13% 1336 201 palmitate (16:0) 0.4822 0.2895  6% Yes 2048950 glucose 0.4829 0.2895 46% 12764 50 Metabolite - 03084 0.4883 0.2907−66%  1493 200 ornithine 0.4906 0.2911 −6% Yes 32595 200 Metabolite -08893 0.4954 0.2929 40% 33968 201 5-dodecenoate (12:1(n-7)) 0.49940.2942 −1% 12761 50 Metabolite - 03081 0.5076 0.298 20% 19374 50Metabolite - 06270 0.5136 0.2988 14% 22116 201 4-methyl-2-oxopentanoate0.5162 0.2988 −52%  33447 201 palmitoleate (16:1(n-7)) 0.5171 0.2988−12%  32656 201 Metabolite - 11339 0.5186 0.2988 −3% 32669 200Metabolite - 11352 0.5188 0.2988 −11%  542 200 3-hydroxybutyrate (BHBA)0.5194 0.2988  1% Yes 31401 50 Metabolite - 10892 0.5325 0.3049 16%32319 50 trans-4-hydroxyproline 0.5336 0.3049 137%  1302 200 methionine0.5401 0.3068 142%  Yes 32855 201 Metabolite - 11538 0.543 0.3068 10%12785 50 Metabolite - 03103 0.5441 0.3068 18% 32553 201 Metabolite -03832 0.5469 0.3073  0% 32869 200 Metabolite - 11552 0.5508 0.308 103% 12782 50 Metabolite - 03100 0.553 0.308 38% 2730 200gamma-glutamylglutamine 0.5534 0.308 71% 1564 50 citrate 0.5579 0.308918% Yes 32761 201 Metabolite - 11444 0.5587 0.3089 15% 32632 200Metabolite - 11315 0.561 0.3092 37% 1605 201 ursodeoxycholate 0.57030.3124  2% 12593 50 Metabolite - 02973 0.5707 0.3124 44% 32885 200Metabolite - 11568 0.5732 0.3124 −4% 32564 201 Metabolite - 11247 0.57430.3124 −9% 3127 200 hypoxanthine 0.5759 0.3124 28% 1444 200 pipecolate0.5811 0.3138 −7% 1644 201 heptanoate 0.5823 0.3138 −11%  33227 201Metabolite - 11882 0.5902 0.3171 13% 54 200 tryptophan 0.598 0.3203 −7%Yes 32418 201 myristoleate (14:1(n-5)) 0.6043 0.3215 95% 15753 201hippurate 0.6049 0.3215  0% Yes 32774 200 Metabolite - 11457 0.61220.3215 −49%  32648 201 Metabolite - 11331 0.6136 0.3215 −4% 27710 50N-acetylglycine 0.615 0.3215 −19%  606 201 uridine 0.6154 0.3215 −4% Yes32797 201 Metabolite - 11480 0.6159 0.3215 35% 31787 2013-carboxy-4-methyl-5-propyl-2- 0.6206 0.3215 −60%  furanpropanoate(CMPF) 32586 200 Metabolite - 01327 0.6213 0.3215 −1% 32348 2002-aminobutyrate 0.6217 0.3215 17% 31489 50 Metabolite - 10914 0.62290.3215  8% 32748 201 Metabolite - 11431 0.6286 0.3235 −21%  32815 201Metabolite - 11498 0.6324 0.3235 −9% 33138 200 Metabolite - 11793 0.63620.3245 32% 12790 50 Metabolite - 03108 0.6404 0.3249 −8% 12035 201pelargonate (9:0) 0.6409 0.3249 28% Yes 27722 50 erythrose 0.6483 0.3277−6% 33901 201 Metabolite - 12456 0.6545 0.3298 −12%  15500 200 carnitine0.6744 0.3389 136%  Yes 33195 201 Metabolite - 11850 0.6796 0.3405 −9%594 201 nicotinamide 0.6834 0.3406 −26%  33638 201 Metabolite - 122170.6867 0.3406 31% 32593 200 Metabolite - 02036 0.6873 0.3406 −13%  1651150 Metabolite - 04274 0.6879 0.3406 −1% 17627 50 Metabolite - 049860.6934 0.3409 −3% 12767 50 Metabolite - 03087 0.696 0.3409 −5% 20694 50oxalate (ethanedioate) 0.6974 0.3409 −8% Yes 27672 201 3-indoxyl sulfate0.6979 0.3409 82% 15676 201 3-methyl-2-oxovalerate 0.6983 0.3409 −9%1561 50 alpha-tocopherol 0.7092 0.3442 −6% Yes 32458 200 oleamide 0.71120.3442  8% 32342 200 adenosine 5′-monophosphate (AMP) 0.7171 0.3458−12%  33131 200 Metabolite - 11786 0.7186 0.3458  4% 33941 200decanoylcarnitine 0.7205 0.3458 19% 27278 50 Metabolite - 10510 0.72540.3472  3% 32970 201 Metabolite - 11653 0.7333 0.35 −12%  32562 201Metabolite - 11245 0.736 0.3503 27% 21631 50 Metabolite - 08403 0.73890.3507 −20%  33230 200 Metabolite - 11885 0.7422 0.3513 −24%  587 50gluconate 0.7493 0.3526 −38%  16508 50 Metabolite - 04272 0.7548 0.3526−8% 33587 201 eicosenoate [9 or 11, cis or trans] 0.7575 0.3526 1446% 24074 50 Metabolite - 09706 0.7579 0.3526 45% 15737 50 glycolate(hydroxyacetate) 0.7599 0.3526 15% Yes 32489 201 caproate (6:0) 0.76240.3526 −24%  32636 201 Metabolite - 11319 0.7652 0.3526 24% 33833 201Metabolite - 12405 0.7667 0.3526 52% 32863 201 Metabolite - 11546 0.770.3526 55% 27738 50 threonate 0.7742 0.3526 23% 63 50 cholesterol 0.77530.3526 104%  Yes 33402 200 Metabolite - 12050 0.7784 0.3526 40% 32651200 Metabolite - 11334 0.7794 0.3526 37% 33265 200 Metabolite - 119200.7797 0.3526  1% 32757 201 Metabolite - 11440 0.78 0.3526 71% 512 50asparagine 0.7878 0.3551 65% Yes 32857 200 Metabolite - 11540 0.79010.3552 54% 31617 50 Metabolite - 10963 0.7929 0.3555 61% 32738 200Metabolite - 11421 0.8008 0.3571 −28%  27256 50 Metabolite - 105000.8059 0.3571 −4% 32558 201 p-cresol sulfate* 0.8079 0.3571  2% 27447201 linoleoylglycerol (monolinolein) 0.808 0.3571 20% 1125 200isoleucine 0.8081 0.3571 −11%  Yes 16837 50 Metabolite - 04507 0.81010.3571 −42%  60 200 leucine 0.8141 0.3572 −5% Yes 1494 200 5-oxoproline0.8154 0.3572 31% Yes 33520 200 Metabolite - 12105 0.819 0.3579 −2% 1110201 arachidonate (20:4(n-6)) 0.8245 0.3594 −14%  Yes 33972 20110-nonadecenoate (19:1(n-9)) 0.8354 0.3613 17% 16666 50 Metabolite -04365 0.8692 0.3741  5% 33971 201 10-heptadecenoate (17:1(n-7)) 0.87630.3761 −16%  27264 50 Metabolite - 10503 0.8865 0.3786 −14%  22570 50Metabolite - 09033 0.8891 0.3788  2% 12771 50 Metabolite - 03091 0.89280.3794 74% 19490 50 Metabolite - 06488 0.8965 0.3801 108%  32548 201Metabolite - 11231 0.9079 0.383  2% 21188 50 stearoylglycerol(monostearin) 0.909 0.383 49% Yes 33488 50 lathosterol 0.9118 0.383 24%15630 200 N-acetylornithine 0.9125 0.383 −5% 21011 50 Metabolite - 078880.9175 0.3837 −5% 32848 201 Metabolite - 11531 0.9202 0.3837 −11%  33163200 Metabolite - 11818 0.9209 0.3837 −11%  15996 50 aspartate 0.92740.3855 −15%  Yes 1649 200 valine 0.9428 0.391 61% Yes 2132 200citrulline 0.9557 0.3939 31% Yes 25602 50 Metabolite - 10432 0.95620.3939 −21%  32760 201 Metabolite - 11443 0.9578 0.3939  9% 33936 200octanoylcarnitine 0.9591 0.3939 −2% 33369 50 Metabolite - 12023 0.97050.3976 −35%  3141 200 betaine 0.9845 0.4022 −18%  32520 200 Metabolite -11206 0.9863 0.4022 −21% 

Pathway trend analysis showed strong differentiation of DCM patients inenergy and lipid pathways, suggesting TCA cycle inhibition, glucogenicamino acid mobilization, and β-oxidation increases. Adrenergic steroids(cortisol, cortisone) were increased, consistent with general stress,and androgen metabolites (DHEA-S) were strongly diminished in DCMpatients, resulting in an apparent metabolic “feminization” of DCMmales.

Comparison to the previous transgenic mouse DCM model plasma studyshowed that eight compounds, including urate, malate, tyrosine,phenylalanine, erythritol, and others exhibited similar responses andwere strongly significant in both studies. Another 16 that were stronglysignificant in the human study trended in a similar manner in the mousestudy. These included a-ketoglutarate, isocitrate, pantothenate,myo-inositol, and glutamate. The data confirm that metabolomic profilesof plasma reflect the disease state in human DCM patients, and that thetransgenic mouse model shares many of the biomarker alterationsassociated with human disease.

T-tests are used to determine if the population means are different, butdo not tell us about individual observations. Random Forest analysis isa multivariate technique for identifying compounds that distinguish theGroups. Random forests are used to classify individuals. Random forestsare based on a consensus of a large number of decision trees; it is anextremely effective multivariate technique, being resistant to outliers,insensitive to method of normalization, and possesses highly predictiveability for new samples. Shown in Table 24 are results of using thebiomarkers listed in Table 23 to classify the subjects as “Healthy” or“DCM”. The subjects are correctly classified as Healthy (Control) 81% ofthe time and correctly classified as having DCM 72% of the time.Subjects are correctly classified with >75% accuracy overall.

TABLE 24 Random Forest Classification of DCM and Healthy subjectsControl DCM error Control 25 6 19% DCM 11 28 28% OOB estimate of errorrate: 24.29%

The biomarkers that are most important to correctly classify subjectsare shown in Table 25 and the Importance plot is shown in FIG. #.

TABLE 25 Important DCM biomarkers Metabolite-11556 4-acetamidobutanoateMetabolite-03951 Choline Metabolite-03056 Metabolite-11335Metabolite-4523 erythronate Metabolite-11593 pseudouridineMetabolite-10395 Metabolite-12095 myo-inositol 3-indolepropionatedeoxycholate Metabolite-11320 Metabolite-3090 Metabolite-59073-(4-hydroxyphenyl)lactate Metabolite-11490 paraxanthineMetabolite-11542 cortisol Metabolite-4361 creatine Metabolite-03653Metabolite-11282 kynurenine3D: Biomarkers of Obesity; Metabolites that are Differentially Presentin Lean Compared to Obese Subjects

Biomarkers were discovered by (1) analyzing blood samples drawn fromdifferent groups of human subjects to determine the levels ofmetabolites in the samples and then (2) statistically analyzing theresults to determine those metabolites that were differentially presentin the two groups.

The plasma samples used for the analysis were from 40 lean subjects(BMI<25) and 40 obese subjects (BMI>30) that had been matched for ageand gender. After the levels of metabolites were determined, the datawas analyzed using univariate T-tests (i.e., Welch's T-test).

T-tests were used to determine differences in the mean levels ofmetabolites between the two populations (i.e., Obese vs. Lean).

Biomarkers:

As listed below in Table 26, biomarkers were discovered that weredifferentially present between samples from obese subjects and leansubjects.

Table 26 includes, for each listed biomarker, the p-value and q-valuedetermined in the statistical analysis of the data concerning thebiomarkers and an indication of the obese mean level, lean mean level,and the ratio of obese mean level to lean mean level (Table 26). Theterm “Isobar” as used in the table indicates the compounds that couldnot be distinguished from each other on the analytical platform used inthe analysis (i.e., the compounds in an isobar elute at nearly the sametime and have similar (and sometimes exactly the same) quant ions, andthus cannot be distinguished). Comp_ID refers to the compoundidentification number used as a primary key for that compound in thein-house chemical database. Library indicates the chemical library thatwas used to identify the compounds. The number 50 refers to the GClibrary and the number 61 refers to the LC library.

TABLE 26 Metabolite biomarkers that are differentially present in obesecompared to lean subjects. Obese/ Mean Mean COMP_ID Library COMPOUNDp-value q-value Lean LEAN OBESE 584 50 mannose <0.0001 <0.0001 1.8730.71 1.33 20489 50 D-glucose <0.0001 <0.0001 1.500 0.78 1.17 18369 61gamma-glu-leu <0.0001 <0.0001 1.407 0.86 1.21 20675 501-5-anhydro-D-glucitol <0.0001 <0.0001 0.629 1.24 0.78 1494 505-oxoproline <0.0001 <0.0001 0.433 1.57 0.68 15365 50sn-glycerol-3-phosphate <0.0001 <0.0001 0.330 1.85 0.61 527 50 lactate<0.0001 <0.0001 0.263 1.86 0.49 22803 61 Isobar-66-includes- <0.00011.00E−04 0.207 3.92 0.81 3127 61 hypoxanthine <0.0001 1.00E−04 0.166 2.90.48 25402 50 Metabolite - 10360 <0.0001 <0.0001 2.544 0.57 1.45 5652 61Metabolite - 1090 <0.0001 <0.0001 0.200 1.7 0.34 7650 61 Metabolite -1834 <0.0001 <0.0001 0.311 1.8 0.56 8959 61 Metabolite - 2100 <0.0001<0.0001 0.202 5.09 1.03 10087 61 Metabolite - 2249 <0.0001 <0.0001 1.8410.82 1.51 11053 61 Metabolite - 2567 <0.0001 <0.0001 1.457 0.81 1.1812667 50 Metabolite - 3034 <0.0001 <0.0001 0.524 1.03 0.54 12969 61Metabolite - 3135 <0.0001 1.00E−04 0.197 4.01 0.79 15278 61 Metabolite -3843 <0.0001 <0.0001 1.547 0.75 1.16 16655 50 Metabolite - 4362 <0.0001<0.0001 2.185 0.65 1.42 16848 50 Metabolite - 4511 <0.0001 <0.0001 0.4601.13 0.52 17028 50 Metabolite - 4611 <0.0001 <0.0001 0.782 1.1 0.8618871 61 Metabolite - 5848 <0.0001 <0.0001 0.380 2.21 0.84 21701 61Metabolite - 8454 <0.0001 <0.0001 5.638 0.47 2.65 21107 615-sulfosalicylate <0.0001 <0.0001 4.667 0.24 1.12 15686 50beta-hydroxypyruvate <0.0001 <0.0001 1.506 0.77 1.16 541 614-hydroxyphenylacetate <0.0001 <0.0001 0.730 1.15 0.84 1303 50 malate<0.0001 <0.0001 0.407 1.94 0.79 8649 61 Metabolite - 2053 <0.0001<0.0001 1.407 0.81 1.14 10433 61 Metabolite - 2293 <0.0001 <0.000110.000 0.13 1.3 11094 61 Metabolite - 2589 <0.0001 <0.0001 8.727 0.110.96 15000 61 Metabolite - 3758 <0.0001 <0.0001 14.091 0.11 1.55 1682150 Metabolite - 4498 <0.0001 <0.0001 0.613 0.93 0.57 17667 61Metabolite - 5026 <0.0001 <0.0001 259.000 0.01 2.59 18010 61Metabolite - 5231 <0.0001 <0.0001 0.425 1.53 0.65 19291 61 Metabolite -6132 <0.0001 <0.0001 3.920 0.25 0.98 19377 50 Metabolite - 6272 <0.0001<0.0001 0.532 1.26 0.67 19508 61 Metabolite - 6549 <0.0001 <0.0001 4.7810.32 1.53 19969 50 Metabolite - 6931 <0.0001 <0.0001 1.605 0.76 1.2221586 50 Metabolite - 8359 <0.0001 <0.0001 2.200 0.5 1.1 21644 61Metabolite - 8406 <0.0001 <0.0001 12.917 0.12 1.55 21648 61 Metabolite -8407 <0.0001 <0.0001 11.636 0.11 1.28 21650 61 Metabolite - 8409 <0.0001<0.0001 10.500 0.12 1.26 21651 61 Metabolite - 8410 <0.0001 <0.000122.600 0.05 1.13 21652 61 Metabolite - 8411 <0.0001 <0.0001 50.400 0.052.52 21653 61 Metabolite - 8412 <0.0001 <0.0001 249.000 0.02 4.98 2165761 Metabolite - 8416 <0.0001 <0.0001 15.000 0.09 1.35 21731 61Metabolite - 8474 <0.0001 1.00E−04 11.929 0.14 1.67 22880 50Metabolite - 9286 <0.0001 <0.0001 1.329 0.82 1.09 2150 61 pyridoxamine1.00E−04 1.00E−04 1.274 0.84 1.07 24285 61 Metabolite - 10026 1.00E−042.00E−04 1.333 0.87 1.16 5702 61 Metabolite - 1114 1.00E−04 1.00E−040.466 1.16 0.54 21630 50 Metabolite - 8402 1.00E−04 1.00E−04 1.422 0.831.18 22590 61 Metabolite - 9040 1.00E−04 2.00E−04 3.783 0.69 2.61 2545950 Metabolite - 10395 1.00E−04 1.00E−04 0.671 1.4 0.94 10049 61Metabolite - 2238 1.00E−04 2.00E−04 6.308 0.39 2.46 12109 61Metabolite - 2853 1.00E−04 1.00E−04 0.475 1.62 0.77 14117 61Metabolite - 3441 1.00E−04 2.00E−04 0.364 2.64 0.96 16506 50Metabolite - 4271 1.00E−04 1.00E−04 0.382 1.57 0.6 17151 61 Metabolite -4656 1.00E−04 1.00E−04 5.833 0.24 1.4 21654 61 Metabolite - 84131.00E−04 1.00E−04 >100 0.001 1.92 2832 61 adenosine-5-monophosphate2.00E−04 2.00E−04 0.291 2.23 0.65 1670 50 urea 4.00E−04 5.00E−04 1.3110.9 1.18 20769 61 maltotriitol 4.00E−04 4.00E−04 0.454 2.18 0.99 1029961 Metabolite - 2274 4.00E−04 4.00E−04 6.146 0.41 2.52 63 50 cholesterol6.00E−04 5.00E−04 1.161 0.93 1.08 1110 50 arachidonic acid 6.00E−045.00E−04 0.685 1.27 0.87 19405 50 Metabolite - 6347 6.00E−04 6.00E−041.679 0.81 1.36 9016 61 Metabolite - 2109 7.00E−04 6.00E−04 1.695 0.951.61 1577 50 2-amino-butyrate 8.00E−04 8.00E−04 1.418 0.91 1.29 12625 50Metabolite - 3002 8.00E−04 8.00E−04 1.482 0.83 1.23 5800 61 Metabolite -1188 0.001 9.00E−04 0.271 2.47 0.67 19397 50 Metabolite - 6326 0.0019.00E−04 1.295 0.95 1.23 12726 50 Metabolite - 3058 0.001 9.00E−04 0.7521.21 0.91 6161 61 Phthalate-possible 0.0011 0.001 1.781 0.96 1.71 1568350 4-methyl-2-oxopentanoate 0.0012 0.0011 1.283 0.92 1.18 18232 50Metabolite - 5403 0.0013 0.0011 1.250 0.92 1.15 18882 61taurodeoxycholic acid 0.0018 0.0015 0.305 2.46 0.75 8509 61 Metabolite -2041 0.0018 0.0015 1.140 0.93 1.06 21188 50 1-stearoyl-rac-glycerol0.0019 0.0016 1.753 0.77 1.35 19490 50 Metabolite - 6488 0.0019 0.00162.033 0.6 1.22 12644 50 Metabolite - 3016 0.002 0.0016 1.242 0.91 1.1325548 50 Metabolite - 10419 0.0021 0.0017 0.865 1.11 0.96 12459 61Isobar-10-includes-glutamine- 0.0028 0.0022 1.168 0.95 1.11H-beta-ala-gly-OH-1- methylguanine-H-Gly-Sar- OH-lysine 1413 613-hydroxyphenylacetate 0.003 0.0024 1.198 0.91 1.09 21047 613-methyl-2-oxobutyrate 0.0032 0.0025 1.519 0.81 1.23 16903 61Metabolite - 4547 0.0033 0.0025 1.505 0.97 1.46 22132 61DL-alpha-hydroxyisocaproic 0.0037 0.0028 0.566 1.22 0.69 acid 21011 50Metabolite - 7888 0.0039 0.0029 1.508 0.65 0.98 10825 61 Metabolite -2546 0.0041 0.003 0.565 1.84 1.04 12912 61 Metabolite - 3129 0.00460.0033 1.589 0.95 1.51 16893 61 Metabolite - 4530 0.0046 0.0034 0.5081.3 0.66 599 61 pyruvate 0.0052 0.0037 0.494 1.78 0.88 1604 61 uric acid0.0054 0.0038 1.095 0.95 1.04 17068 61 Metabolite - 4627 0.0056 0.00390.286 2.69 0.77 17614 50 Metabolite - 4966 0.006 0.0042 1.454 0.97 1.4119934 50 inositol 0.0062 0.0043 0.831 1.18 0.98 12673 50 Metabolite -3040 0.0063 0.0043 0.592 1.79 1.06 10551 61 Metabolite - 2347 0.0070.0048 0.235 3.27 0.77 22189 61 palmitoyl-carnitine 0.0076 0.0051 0.7251.31 0.95 22020 50 Metabolite - 8749 0.0077 0.0052 0.406 2.34 0.95 949161 Metabolite - 2185 0.0081 0.0054 1.277 0.94 1.2 22602 50 Metabolite -9045 0.0097 0.0064 0.673 1.07 0.72 16071 50 Metabolite - 4020 0.01030.0067 0.826 1.21 1 15677 61 3-methyl-L-histidine 0.0107 0.007 1.289 0.91.16 18476 61 glycocholic acid 0.0107 0.007 0.223 3.37 0.75 16496 50Metabolite - 4251 0.0122 0.0078 0.798 1.19 0.95 597 61phosphoenolpyruvate 0.0127 0.008 0.735 1.02 0.75 6851 61 Metabolite -1497 0.0128 0.008 1.242 0.91 1.13 15650 61 1-methyladenosine 0.0150.0093 1.117 0.94 1.05 22026 50 1-methylguanidine- 0.0151 0.0093 1.1580.95 1.1 hydrochloride 12774 50 Metabolite - 3094 0.0173 0.0106 0.8421.14 0.96 7944 61 Metabolite - 1915 0.0176 0.0107 2.514 1.38 3.47 1483761 Metabolite - 3707 0.0179 0.0108 1.911 1.12 2.14 13589 61 Metabolite -3327 0.0182 0.0109 0.374 2.97 1.11 9905 61 Metabolite - 2231 0.01830.0109 1.195 0.87 1.04 12648 50 Metabolite - 3020 0.0192 0.0115 0.7451.41 1.05 10604 61 Metabolite - 2370 0.0231 0.0136 0.719 1.35 0.97 1738950 Metabolite - 4796 0.0238 0.014 0.332 2.65 0.88 14715 61Stachydrine-possible 0.0257 0.015 0.345 3.54 1.22 1574 61 histamine0.0264 0.0152 0.841 1.07 0.9 15113 61 Metabolite - 3783 0.0268 0.01531.268 0.97 1.23 19514 61 Metabolite - 6553 0.0272 0.0155 1.620 0.5 0.8120842 61 Metabolite - 7765 0.0288 0.0162 0.513 2.32 1.19 20194 61Metabolite - 7147 0.0289 0.0162 0.887 1.06 0.94 5577 61 Metabolite -1065 0.0323 0.0179 2.106 1.41 2.97 15227 61 Metabolite - 3816 0.03230.0179 0.767 1.29 0.99 15140 61 L-kynurenine 0.0329 0.0182 1.143 0.981.12 9748 61 Metabolite - 2212 0.034 0.0187 0.723 1.41 1.02 5765 615-hydroxypentanoate-or-beta- 0.036 0.0197 0.562 2.01 1.13hydroxyisovaleric acid- possible 19372 50 Metabolite - 6269 0.038 0.02070.857 0.98 0.84 11111 61 Metabolite - 2592 0.0404 0.0216 2.600 0.9 2.3420166 61 Metabolite - 7091 0.0415 0.0219 1.443 1.06 1.53 22133 61DL-hexanoyl-carnitine 0.0429 0.0224 1.240 1 1.24 10629 61 Metabolite -2386 0.0467 0.0242 1.289 0.83 1.07 15122 50 glycerol 0.048 0.0248 1.1750.97 1.14 1643 50 fumarate 0.051 0.0262 0.903 1.03 0.93 22337 61Metabolite - 8893 0.0518 0.0265 1.153 0.98 1.13 8469 61 Metabolite -2036 0.0521 0.0265 6.559 1.11 7.28 5687 61 Metabolite - 1110 0.05470.0274 1.648 1.25 2.06 15500 61 carnitine 0.0591 0.0294 0.821 1.17 0.9617048 61 Metabolite - 4617 0.0614 0.0303 1.083 0.96 1.04 1105 50Linoleic acid 0.0647 0.0316 0.882 1.1 0.97 20699 50 meso-erythritol0.0703 0.0341 0.901 1.11 1 21763 61 Metabolite - 8507 0.0712 0.03420.779 1.04 0.81 7941 61 Metabolite - 1914 0.0724 0.0346 1.549 0.82 1.271336 50 n-hexadecanoic acid 0.0795 0.0379 0.885 1.13 1 9130 61Metabolite - 2139 0.0815 0.0386 1.212 0.99 1.2 12720 61 Metabolite -3056 0.0891 0.0421 0.847 1.18 1 17783 61 trans-2-3-4- 0.0906 0.04251.525 0.8 1.22 trimethoxycinnamic acid 16226 61 Isobar-28-includes-L-0.0908 0.0425 0.871 1.16 1.01 threonine-L-allothreonine-L-homoserine-S-4-amino-2- hydroxybutyric acid 1358 50 octadecanoic acid0.099 0.0461 0.916 1.07 0.98 542 50 3-hydroxybutanoic acid 0.1004 0.04660.624 1.97 1.23 6266 61 Metabolite - 1286 0.1007 0.0466 1.052 0.97 1.0218392 61 theobromine 0.1072 0.0494 1.629 1.05 1.71 20950 50 Metabolite -7846 0.1136 0.0521 0.579 2.35 1.36 18349 61 DL-indole-3-lactic acid0.1144 0.0522 0.857 1.19 1.02 10245 61 Metabolite - 2269- 0.1162 0.05221.487 1.13 1.68 17304 61 Metabolite - 4759 0.1162 0.0522 1.233 0.9 1.1118034 61 Metabolite - 5234 0.1162 0.0522 1.295 1.12 1.45 22001 613-hydroxyoctanoate 0.1208 0.0541 0.773 1.32 1.02 1572 50 glyceric acid0.126 0.0555 0.874 1.11 0.97 12856 61 Metabolite - 3123 0.1261 0.05552.924 0.79 2.31 6497 61 Metabolite - 1374 0.1277 0.056 1.402 1.02 1.4316650 50 Metabolite - 4360 0.1292 0.0565 0.713 1.29 0.92 10286 61Metabolite - 2272 0.1333 0.0578 0.625 1.68 1.05 18657 61 Metabolite -5726 0.1363 0.0588 0.816 1.14 0.93 15681 61 4-Guanidinobutanoic acid0.1368 0.0588 0.896 1.06 0.95 8176 61 Metabolite - 1974 0.1394 0.05951.260 0.96 1.21 21418 61 Isobar-56-includes-DL- 0.1529 0.0643 0.768 1.641.26 pipecolic acid-1-amino-1- cyclopentanecarboxylic acid 12710 61Metabolite - 3052 0.1538 0.0644 0.920 1 0.92 13545 61 Metabolite - 33220.1626 0.0673 0.730 1.78 1.3 10715 61 Metabolite - 2395 0.163 0.06731.527 1.29 1.97 17478 61 Metabolite - 4873 0.1635 0.0673 1.481 0.81 1.219097 61 Metabolite - 5969 0.1709 0.0698 1.174 0.92 1.08 18963 61Metabolite - 5918 0.1718 0.0699 2.337 1.01 2.36 24076 50 Metabolite -9726 0.1767 0.0714 1.110 1 1.11 17093 61 Metabolite - 4642 0.1812 0.07291.146 0.96 1.1 10746 61 Isobar-6-includes-valine- 0.1889 0.0757 1.1480.88 1.01 betaine 6362 61 p-cresol-sulfate 0.1896 0.0758 0.822 1.18 0.9710092 61 Metabolite - 2250 0.1908 0.0759 0.636 2.06 1.31 22261 61Isobar-60-includes-s-2- 0.1987 0.0785 1.288 0.73 0.94 hydroxybutyrate-2-hydroxyisobutyrate 5733 61 Metabolite - 1127 0.201 0.0792 0.884 1.120.99 20830 61 Metabolite - 7762 0.2051 0.0802 1.357 0.98 1.33 10700 61Metabolite - 2393 0.2053 0.0802 0.928 1.11 1.03 22120 61 6-gamma-gamma-0.2065 0.0802 0.951 1.03 0.98 dimethylallyl-amino-purine 16518 50Metabolite - 4276 0.2065 0.0802 1.154 0.91 1.05 18829 61 phenylalanine0.2149 0.0832 0.952 1.05 1 14439 61 Metabolite - 3498 0.2271 0.08761.072 0.97 1.04 8300 61 Metabolite - 1988 0.2284 0.0878 1.165 1.03 1.26421 61 Metabolite - 1345 0.2299 0.088 1.341 1.23 1.65 1508 61pantothenic acid 0.2315 0.0884 1.167 1.02 1.19 15121 61 Metabolite -3786 0.2338 0.0889 0.763 1.18 0.9 18394 61 theophylline 0.2362 0.08931.420 1.19 1.69 12626 50 Metabolite - 3003 0.2364 0.0893 0.901 1.01 0.916492 61 Metabolite - 1371 0.2409 0.0907 1.369 1.11 1.52 6413 61phenylacetylglutamine-or- 0.2441 0.0916 0.836 1.28 1.07formyl-N-acetyl-5- methoxykynurenamine- possible 16662 61 Metabolite -4363 0.2586 0.0967 0.787 1.41 1.11 24077 50 Metabolite - 9727 0.27260.1016 0.789 1.47 1.16 22259 61 Isobar-59-includes-N-6- 0.281 0.10411.106 0.94 1.04 trimethyl-L-lysine-H- homoarg-OH 15676 503-methyl-2-oxovaleric acid 0.283 0.1041 0.912 0.91 0.83 13208 61Metabolite - 3181 0.2918 0.107 0.904 1.04 0.94 10787 61 Metabolite -2507 0.2989 0.1092 1.308 1.04 1.36 18705 61 Metabolite - 5768 0.30120.1097 0.822 1.35 1.11 16865 50 Metabolite - 4522 0.314 0.1133 1.0410.97 1.01 12756 50 Metabolite - 3077 0.3211 0.1151 1.059 1.01 1.07 1690961 Metabolite - 4549 0.3323 0.1179 0.716 1.41 1.01 18702 61 Metabolite -5767 0.3381 0.1196 1.118 0.93 1.04 569 61 caffeine 0.3393 0.1196 0.6833.82 2.61 1507 50 palmitoleic acid 0.34 0.1196 0.826 1.21 1 20248 61Metabolite - 7177 0.3483 0.1217 1.317 1.01 1.33 15253 61 Metabolite -3832-possible- 0.3576 0.1246 1.341 1.64 2.2 phenol-sulfate 1645 50n-dodecanoate 0.3717 0.1291 0.922 1.16 1.07 22577 50 Metabolite - 90350.384 0.1329 0.897 1.16 1.04 20267 61 Metabolite - 7187 0.3923 0.13521.296 1.15 1.49 7933 61 Metabolite - 1911 0.4027 0.1377 0.702 2.25 1.5817066 61 Metabolite - 4626 0.4028 0.1377 0.935 1.08 1.01 15529 61Metabolite - 3951 0.4084 0.1391 0.962 1.05 1.01 513 61 creatinine 0.4130.1399 0.971 1.04 1.01 8072 61 Metabolite - 1958 0.4193 0.1416 0.9711.02 0.99 1564 50 citric acid 0.4489 0.1507 0.912 1.13 1.03 15737 50hydroxyacetic acid 0.4522 0.1514 0.953 1.06 1.01 18015 61 Metabolite -A-3113 0.4749 0.1567 0.879 1.07 0.94 13142 61 Metabolite - 3165 0.47630.1567 0.962 1.05 1.01 24233 61 Metabolite - 9855 0.4766 0.1567 0.7602.04 1.55 15663 61 2-3-dihydroxybenzoic acid 0.4858 0.1583 0.875 1.361.19 21421 50 Metabolite - 8214 0.487 0.1583 1.040 1.01 1.05 16070 50Metabolite - 4019 0.487 0.1583 0.960 1.01 0.97 12478 61 Metabolite -2898 0.4959 0.16 1.384 1.51 2.09 17271 61 Metabolite - 4746 0.4968 0.161.052 0.97 1.02 1417 61 Kynurenic acid 0.5074 0.162 1.066 1.06 1.1311438 50 phosphate 0.5236 0.1667 0.980 1 0.98 14840 61 Metabolite - 37080.5402 0.171 0.887 1.15 1.02 17665 61 p-hydroxybenzaldehyde 0.54510.1711 1.020 1 1.02 54 61 tryptophan 0.5459 0.1711 0.981 1.03 1.01 1501761 Metabolite - 3761 0.5464 0.1711 0.934 1.06 0.99 13179 61 Metabolite -3176-possible- 0.5563 0.1732 0.936 1.1 1.03 creatine 14961 61Metabolite - 3752 0.5662 0.1758 1.064 0.94 1 17298 61 Metabolite - 47560.571 0.1768 1.071 0.98 1.05 22053 61 3-hydroxydecanoic acid 0.57410.1773 0.917 1.08 0.99 10317 61 Metabolite - 2279 0.5777 0.1779 0.8491.39 1.18 7029 61 Metabolite - 1597 0.5816 0.1786 1.020 1 1.02 16244 61Isobar-21-includes-gamma- 0.5964 0.1816 0.952 1.05 1aminobutyryl-L-histidine-L- anserine 21044 50 Metabolite - s-2- 0.61180.1857 1.064 1.09 1.16 hydroxybutyric acid 10501 61 Metabolite - 23210.6132 0.1857 1.099 1.21 1.33 19787 61 Metabolite - 6746 0.62 0.1870.981 1.05 1.03 1301 50 lysine 0.6207 0.187 0.922 1.29 1.19 16939 61Metabolite - 4586 0.6303 0.1889 1.032 0.93 0.96 19906 61 Metabolite -6827 0.6345 0.1896 1.071 1.12 1.2 10304 61 Metabolite - 2276 0.63990.1897 1.164 1.34 1.56 22145 61 O-acetyl-L-carnitine- 0.6448 0.19 1.0291.04 1.07 hydrochloride 5809 61 3-indoxyl-sulfate 0.646 0.19 0.936 1.11.03 18706 61 Metabolite - 5769 0.6599 0.1935 0.963 1.08 1.04 12604 50Metabolite - 2981 0.6621 0.1936 1.020 0.99 1.01 17488 61 Metabolite -4887 0.6822 0.1975 0.966 0.89 0.86 1299 61 tyrosine 0.7135 0.2055 1.0201.01 1.03 22154 61 bradykinin 0.7224 0.2069 0.925 1.46 1.35 606 61uridine 0.7239 0.2069 1.020 1.01 1.03 12035 50 nonanate 0.7322 0.20870.990 0.99 0.98 6144 61 Metabolite - 1215 0.7562 0.2145 0.861 3.88 3.3421762 61 Metabolite - 8506 0.785 0.222 0.939 1.65 1.55 1506 61orotidine-5-phosphate 0.789 0.2226 0.971 1.03 1 13038 61 Metabolite -3143 0.7911 0.2227 0.943 1.23 1.16 2734 61 gamma-L-glutamyl-L- 0.81580.2262 1.021 0.97 0.99 tyrosine 12924 61 Metabolite - 3131 0.8273 0.22881.033 1.22 1.26 1642 50 decanoic acid 0.8396 0.2305 1.022 0.93 0.9522895 50 Metabolite - 9299 0.8504 0.2323 1.023 0.86 0.88 16016 61Metabolite - 3994 0.8777 0.2388 1.024 0.84 0.86 15255 61 Metabolite -3833 0.8785 0.2388 1.040 1.24 1.29 13146 61 Metabolite - 3166 0.88210.2392 0.970 0.99 0.96 17033 61 Metabolite - 4613 0.8939 0.2412 1.0210.95 0.97 15753 61 hippuric acid 0.9065 0.2441 1.022 1.38 1.41 15612 61Metabolite - 3972 0.9499 0.2546 1.000 0.97 0.97 18254 611-7-dimethylxanthine 0.9503 0.2546 1.026 1.94 1.99 594 61 niacinamide0.9552 0.2548 1.024 0.83 0.85 15326 61 Metabolite - 3879 0.9567 0.25480.985 1.31 1.29 15765 61 ethylmalonic acid 0.9909 0.2611 1.000 0.96 0.961570 50 oleic acid 0.9978 0.2623 1.000 1.03 1.03

3E: Algorithms (Models) for Diagnosing Metabolic Syndrome andPre-Disposition to Metabolic Syndrome (Insulin Sensitivity).

Models were developed to test the ability to predict insulin sensitivity(Rd) and metabolic syndrome using the biomarker metabolites alone and/orin combination with clinical measures of metabolic syndrome (e.g. BMI,Rd). The plasma samples used for the analysis were from subjects withvarious rates of glucose disposal (Rd).

Algorithms for determining insulin sensitivity were developed bymultiple iterations of regression analysis of glucose utilization rate(i.e. Rd) in combination with measurements of metabolite biomarkers. Thesamples were divided into two groups. The first group was used as a‘training’ set and the second group was used as a ‘test’ set. Then amodel was developed using the training set and the predictive power ofthe resulting model was determined using the test set. Several modelswere developed to identify the most important biomarker metabolites forpredicting insulin sensitivity and thereby demonstrating the utility ofthis approach and these biomarker metabolites.

A model was developed to predict insulin sensitivity (i.e. Rd) usingplasma samples collected from a cohort with varying levels of insulinsensitivity and BMI less than 27.9 and BMI greater than 27.9. For thismodel the training group included half of the plasma samples and wasbalanced for BMI and Rd. The model was then tested using a test groupthat included the other half of the samples and was also balanced forBMI and Rd. The results of this analysis showed that the best model forpredicting insulin sensitivity includes: BMI and the biomarkermetabolites glucose, 3-methyl-2-oxobutyric acid, 1,5-anhydroglucitol andmetabolite-6268.

Another model was developed to predict insulin sensitivity (i.e. Rd)using plasma samples collected from a cohort with varying levels ofinsulin sensitivity and BMI less than 27.9. For this model the traininggroup included half of the plasma samples and was balanced for insulinsensitivity (Rd). The model was then tested using a test group thatincluded the other half of the plasma samples and was also balanced forRd. The results of this analysis showed that the best model forpredicting insulin sensitivity includes the biomarker metabolites:glucose, metabolite-2546, metabolite-2853, metabolite-2370 andmetabolite-2386.

Yet another model was developed to predict insulin sensitivity (i.e. Rd)using plasma samples collected from a cohort with varying levels ofinsulin sensitivity and BMI greater than 27.9. For this model thetraining group included half of the plasma samples and was balanced forinsulin sensitivity (Rd). The model was then tested using a test groupthat included the other half of the plasma samples and was also balancedfor Rd. The results of this analysis showed that the best model forpredicting insulin sensitivity includes the biomarker metabolites:3-methyl-2-oxobutyric, metabolite-3097, metabolite-4020, metabolite-3056and metabolite-1831.

The model: BMI and the biomarker metabolites glucose,3-methyl-2-oxobutyric acid, 1,5-anhydroglucitol and metabolite-6268; wasused on a new cohort to predict insulin sensitivity (Rd). The model wasdeveloped to predict insulin sensitivity (i.e. Rd) using plasma samplescollected from a cohort with varying levels of insulin sensitivity andBMI less than 27.9 and BMI greater than 27.9, as described above. Thesamples used to test the model for this analysis were obtained from 19Caucasian males aged 18-39, average age of 25.6, that had been diagnosedwith metabolic syndrome and 19 healthy, age-matched, Caucasian males.Plasma samples and serum samples were evaluated. The results of thisanalysis show that the model could correctly predict insulin sensitivityin this new cohort using either plasma (FIG. 21) or serum (FIG. 22)samples.

Example 4 Treatment Response Biomarkers

Biomarkers that are predictive of response to treatment were identifiedthrough comparisons of subjects that were ‘non-responders’ (i.e. thosewith little or no change (<15%) in Rd between baseline and 12 weekspost-treatment) and those subjects that were responsive to the treatment(i.e. ‘responders’). Biomarkers that were predictive of subjects astreatment responders or non-responders were based on compound levels atbaseline only. The responders were defined either as those subjects witheither a Rd change of 35% or higher or as those with a Rd change of 15%or higher. Data was analyzed by comparing Non-responders with bothclasses of Responders. Both analyses (i.e. Non-responder with Rd changeunder 15% vs Responder with Rd change over 35%, Non-responder with Rdchange under 15% vs Responder with Rd change over 15%) were thencombined and those biomarkers with a p value of <0.1 in EITHER of the 2analyses were identified. The biomarkers are listed in Table 27. Thebiomarker measurements before treatment were predictive ofthiazolidinedione (TZD) response, and thus can be used to selectpatients for treatment with TZD drugs. Experiments are planned toevaluate the biomarkers as predictive to other treatments for insulinsensitivity, pre-diabetes and diabetes control such as other therapeuticagents (e.g. metformin, etc.), weight loss, nutrition and otherlifestyle modifications. This group of predictive biomarkers provide anextremely valuable tool for personalized medicine.

TABLE 27 Biomarkers to Classify Responders or Non-Responders ofTreatment P value (Responder vs Non- COMPOUND LIB_ID COMP_ID Responder)Metabolite-11737 200 33082 0.0007 Metabolite-11849 201 33194 0.0013inositol 50 19934 0.0041 glycerophosphorylcholine (GPC) 200 15990 0.0043phenylalanine 200 64 0.0045 acetylcarnitine 200 32198 0.0049 linoleate(18:2(n-6)) 201 32673 0.0079 Metabolite-11845 201 33190 0.0098tryptophan 200 54 0.0107 Metabolite-10407 50 25522 0.0113Metabolite-11379 201 32696 0.0117 Metabolite-9727 50 24077 0.0136Metabolite-11205 200 32519 0.0151 Metabolite-11883 200 33228 0.0168Metabolite-10954_200 200 32734 0.0178 glycerol 50 15122 0.0182gondoate-20-1-n-9- 201 32402 0.0182 Metabolite-03832_201 201 325530.0209 oleate (18:1(n-9)) 201 32630 0.0225 Metabolite-11793 200 331380.0225 gamma-glutamylphenylalanine- 200 33362 0.0231 Metabolite-11560201 32877 0.0242 Metabolite-11247 201 32564 0.0273 Metabolite-11887 20133232 0.0317 Metabolite-11206 200 32520 0.0317 tyrosine 200 1299 0.0317Metabolite-12064 201 33416 0.0317 EDTA* 201 32511 0.0317Metabolite-11790 200 33135 0.0338 3-hydroxybutyrate (BHBA) 50 542 0.0339palmitoleate (16:1(n-7)) 201 32628 0.0412 lysine 50 1301 0.0468Metabolite-11314 200 32631 0.0468 Metabolite-11204 200 32518 0.0468Metabolite-11437 201 32754 0.0491 alpha linolenate (18:3(n-3)) 201 324160.0499 peptide- 200 31548 0.0531 DSGEGDFXAEGGGVR Metabolite-11874 20133219 0.0531 palmitate (16:0) 201 1336 0.0547 methionine 201 1302 0.0566glycerol 3-phosphate (G3P) 50 15365 0.0566 Metabolite-2800 50 162870.0593 Metabolite-11522 201 32839 0.0611 Metabolite-11421 200 327380.062 Metabolite-11881 201 33226 0.0637 4-methyl-2-oxopentanoate 20122116 0.0637 quinate 50 18335 0.0649 pelargonate-9-0- 201 12035 0.0676creatinine 200 513 0.0676 3-methyl-2-oxobutyrate 201 21047 0.0676Metabolite-11235 201 32552 0.0692 Metabolite-6272 50 19377 0.071Metabolite-10360 50 25402 0.0726 saccharin 201 21151 0.0734Metabolite-11593 201 32910 0.0742 ornithine 50 1493 0.076 cholate 20122842 0.0775 1,6-anhydroglucose 50 21049 0.0787 Metabolite-11435 20132752 0.0829 Metabolite-11832 201 33177 0.0891 deoxycholate 201 11140.0902 Metabolite-11880 201 33225 0.0902 docosahexaenoate (DHA) 50 193230.0927 Metabolite-12056 200 33408 0.0981 Metabolite-3075 50 12754 0.0987

Recursive pardoning analysis was carried out on the subjects. Baselinelevels (i.e. prior to treatment) of the biomarker compounds weredetermined in the Responders (subjects with a post-treatment increase inRd ≧35%, N=28) and the Non-Responders (subjects with a post-treatment Rdincrease of <15%, N=14). The results of this analysis showed that thesubjects were classified with an AUC of 0.8214. The analysis furtheridentified “Metabolite-11737” as a biomarker that is particularlyimportant in the classification of responders and non-responders. Usingthe baseline level of only this biomarker, 22 of 28 Responders werecorrectly classified and 12 of 14 Non-Responders were correctlyclassified. This marker alone had a sensitivity of 78.6% and aspecificity of 85.7%. The Positive Predictive Value (PPV) was 91.7% andthe Negative Predictive Value (NPV) was 66.7%.

Biomarkers that are pharmacodynamic (PD) biomarkers of treatmenteffectiveness were identified through comparisons of non-responders(i.e. those with little or no change (<15%) in Rd between baseline and12 weeks post-treatment) vs responders. The PD biomarkers were based onthe difference between baseline and 12 weeks post-treatment. Biomarkerswere identified that showed changes from baseline levels upon TZDtreatment and tracked with the change in insulin sensitivity in thosesubjects. These biomarkers are listed in Table 28.

TABLE 28 Pharmacodynamic Biomarkers of Treatment Response P-Value(Responder vs COMPOUND COMP_ID LIB_ID Non-Responder) Metabolite - 1173733082 200 0.0007 Metabolite - 11849 33194 201 0.0013 palmitoylglycerol21127 50 0.0041 (monopalmitin) glutamate 12751 50 0.0041glycerophosphorylcholine 15990 200 0.0043 (GPC) acetylcarnitine 32198200 0.0049 gamma- 33362 200 0.0052 glutamylphenylalaninealpha-tocopherol 1561 50 0.0053 glucose 20488 50 0.0062 phenylalanine 64200 0.0071 inositol 19934 50 0.0075 Metabolite - 9727 24077 50 0.0079Metabolite - 11385 32702 200 0.0083 Metabolite - 11845 33190 201 0.0098tryptophan 54 200 0.0107 Metabolite - 10407 25522 50 0.0113 erythritol20699 50 0.0136 Metabolite - 11205 32519 200 0.0151 Metabolite - 1188333228 200 0.0168 Metabolite - 10954 32734 200 0.0178 methionine 1302 2010.0196 Metabolite - 03832 32553 201 0.0209 5-oxoproline 1494 200 0.0242Metabolite - 4611 17028 50 0.0242 linoleate (18:2(n-6)) 32673 201 0.0258galactonic acid 27719 50 0.0259 Metabolite - 11247 32564 201 0.0273dipalmitin 27392 50 0.0297 tyrosine 1299 200 0.0317 Metabolite - 1137932696 201 0.035 Metabolite - 11560 32877 201 0.0387 Metabolite - 1143732754 201 0.0491 Metabolite - 11206 32520 200 0.0492 Metabolite - 1147532792 201 0.0564 Metabolite - 11254 32571 200 0.0564 Metabolite - 1061027889 50 0.0564 glycerol 3-phosphate (G3P) 15365 50 0.0566gondoate-20-1-n-9 32402 201 0.0566 Metabolite - 2800 16287 50 0.0593Metabolite - 11386 32703 200 0.06 Metabolite - 4055 16120 50 0.06Metabolite - 11421 32738 200 0.062 Metabolite - 11244 32561 201 0.0637Metabolite - 6486 19487 50 0.0637 Metabolite - 7846 20950 50 0.0637quinate 18335 50 0.0649 Metabolite - 11881 33226 201 0.06493-methyl-2-oxovalerate 15676 201 0.0676 Metabolite - 9045 22602 500.0676 Metabolite - 4360 16650 50 0.0676 Metabolite - 11788 33133 2000.0676 Metabolite - 6272 19377 50 0.071 3-hydroxybutyrate (BHBA) 542 500.071 oleate (18:1(n-9)) 32630 201 0.071 Metabolite - 10360 25402 500.0726 saccharin 21151 201 0.0734 Metabolite - 11593 32910 201 0.0742Metabolite - 11790 33135 200 0.0749 kynurenine 15140 200 0.076Metabolite - 4357 16634 50 0.076 Metabolite - 3100 12782 50 0.076cholate 22842 201 0.0775 1,6-anhydroglucose 21049 50 0.0787 Metabolite -11887 33232 201 0.081 glycerol 15122 50 0.081 Metabolite - 06126 32557201 0.0829 Metabolite - 4986 17627 50 0.0853 Metabolite - 3099 12781 500.0902 Metabolite - 11314 32631 200 0.0921 docosahexaenoate (DHA) 1932350 0.0927 Metabolite - 11522 32839 201 0.0954 Metabolite - 01142 32747201 0.0954 Metabolite - 3075 12754 50 0.0987

Recursive pardoning analysis was carried out on the subjects. Baselinelevels (i.e. prior to treatment) and post-treatment levels of thebiomarker compounds were determined in the Responders (subjects with apost-treatment increase in Rd≧35%, N=28) and the Non-Responders(subjects with a post-treatment Rd increase of <15%, N=14). The resultsof this analysis showed that the subjects were classified with an AUC of0.7679. Additional experiments are planned to evaluate the biomarkers asPD biomarkers for other insulin sensitivity, pre-diabetes and diabetestherapeutic agents (e.g. metformin, etc.) as well as treatmentsinvolving modification to diet (e.g. weight loss, nutrition) andlifestyle (e.g. exercise).

Example 5 Analytical Characterization of Unnamed Biomarkers Compounds

Table 29 below includes analytical characteristics of each of theunnamed metabolites listed in the Tables above. The table includes, foreach listed Metabolite, the retention time (RT), retention index (RI),mass, quant mass, and polarity obtained using the analytical methodsdescribed above. “Mass” refers to the mass of the C12 isotope of theparent ion used in quantification of the compound. “Polarity” indicatesthe polarity of the quantitative ion as being either positive (+) ornegative (−).

TABLE 29 Analytical characteristics of biomarker metabolites. COMP_IDCOMPOUND LIBRARY RT RI MASS POLARITY 25532 Metabolite - 10413 50 12.532042.7 204.1 +L 25602 Metabolite - 10432 50 12.29 2031.5 204 +L 27256Metabolite - 10500 50 5.3 1229.9 211 +L 27264 Metabolite - 10503 50 7.281452.4 244 +L 27889 Metabolite - 10610 50 11.93 1987 204 +L 30288Metabolite - 10750 50 5.51 1265 102.9 +L 30290 Metabolite - 10752 506.07 1323.7 231 +L 30832 Metabolite - 10814 50 12.84 2094 204.1 +L 31373Metabolite - 10878 50 8.22 1583 334.1 +L 31509 Metabolite - 10931 5012.02 1984 174.1 +L 31518 Metabolite - 10933 50 11.99 1979 318.1 +L12781 Metabolite - 3099 50 11.77 2002.3 204 +L 12782 Metabolite - 310050 11.85 2010.7 204 +L 12795 Metabolite - 3113 50 12.73 2111.4 406.2 +L16120 Metabolite - 4055 50 12.04 2021.4 304.1 +L 16138 Metabolite -4080 - 50 14.02 2266.9 299 +L retired for 1-palmitoyl- sn-glycero-3-phosphocholine 16509 Metabolite - 4273 50 10.34 1844.2 457.2 +L 16511Metabolite - 4274 50 10.37 1854.7 158.1 +L 16512 Metabolite - 4275 - 5010.68 1884.6 345.2 +L retired-part of X- 3078 16518 Metabolite - 4276 -50 13.92 2261 223.1 +L retired for gamma- tocopherol* 16634 Metabolite -4357 50 8 1540.5 216 +L 16650 Metabolite - 4360 50 9.15 1678.4 347.2 +L16665 Metabolite - 4364- 50 10.66 1852.1 232 +L retired for L-asparagine - 3 16666 Metabolite - 4365 50 11.05 1893.1 204 +L 16829Metabolite - 4503 50 8.39 1589.3 227.2 +L 17028 Metabolite - 4611- 508.07 1546.9 292.1 +L retired for erythronic acid* 17330 Metabolite -4769 50 11.3 1916.6 156 +L retired for glutamine - 2 17389 Metabolite -4796 50 3.53 1043.2 117 +L 17690 Metabolite - 5207 50 7.41 1493.6 151 +L18120 Metabolite - 5348 50 9.25 1681.5 217.9 +L 19462 Metabolite - 644650 3.49 1021.1 204.1 +L 19478 Metabolite - 6467 50 11.09 1893.4 320.1 +L19487 Metabolite - 6486 50 11.6 1949.8 217 +L 19576 Metabolite - 6627 5011.96 1990.7 304.2 +L 19983 Metabolite - 6955 50 11.82 1979.1 306.1 +L12162 Metabolite - A-2339 50 3.86 1109.8 221.0 +L 12222 Metabolite -A-2374 50 7.35 1510.9 188.0 +L 12803 Metabolite - A-2441 50 13.94 2270.9129.0 +L 16074 Metabolite - A-2758 50 8.22 1597.1 211.0 +L 16285Metabolite - A-2798 50 3.44 1005.8 163.0 +L 16287 Metabolite - A2800 503.53 1015.5 191.1 +L 24360 Metabolite -10206 50 9.04 1639.0 243.0 +L25402 Metabolite -10360 50 10.23 1780.0 204.0 +L 25429 Metabolite -1036950 10.92 1859.0 333.0 +L 25459 Metabolite -10395 50 9.94 1768.9 156.0 +L25522 Metabolite -10407 50 9.94 1748.0 217.1 +L 25548 Metabolite -1041950 16.29 2527.0 311.3 +L 25584 Metabolite -10425 50 7.52 1488.8 123.9 +L25597 Metabolite -10427 50 11.21 1911.1 183.0 +L 25598 Metabolite -1042850 11.31 1922.0 156.0 +L 25599 Metabolite -10429 50 11.60 1953.5 265.0+L 25607 Metabolite -10437 50 8.43 1596.0 331.1 +L 25609 Metabolite-10439 50 8.84 1643.3 331.1 +L 25649 Metabolite -10450 50 17.00 2643.0371.3 +L 27137 Metabolite -10498 50 12.06 1991.8 299.1 +L 27271Metabolite -10504 50 9.94 1763.0 348.2 +L 27272 Metabolite -10505 5010.82 1862.0 457.3 +L 27273 Metabolite -10506 50 11.30 1914.0 218.1 +L27275 Metabolite -10507 50 11.97 1988.0 370.2 +L 27278 Metabolite -1051050 15.77 2470.0 297.2 +L 27279 Metabolite -10511 50 17.12 2645.0 309.3+L 27288 Metabolite -10517 50 10.16 1775.0 419.2 +L 27326 Metabolite-10527 50 11.71 1950.0 221.1 +L 27678 Metabolite -10584 - 50 10.151779.0 217.0 +L retired for glucose-3 27841 Metabolite -10595 50 4.141101.0 151.0 +L 27888 Metabolite -10609 50 11.70 1961.0 348.2 +L 27890Metabolite -10611 50 12.03 1998.0 369.1 +L 28059 Metabolite -10650 5010.26 1800.6 345.1 +L 29817 Metabolite -10683 50 5.12 1213.8 171.0 +L30265 Metabolite -10732 50 12.22 2024.0 204.0 +L 30273 Metabolite -1073650 10.03 1814.0 342.1 +L 30282 Metabolite -10744 50 15.75 2503.0 311.2+L 12533 Metabolite -2915 50 3.77 1099.0 174.0 +L 12593 Metabolite -297350 4.74 1213.4 281.0 +L 12604 Metabolite -2981 50 5.21 1265.2 211.0 +L12609 Metabolite -2986 50 5.56 1304.3 201.0 +L 12625 Metabolite -3002 506.74 1440.8 296.0 +L 12626 Metabolite -3003 50 6.79 1446.6 218.0 +L12638 Metabolite -3011 50 7.08 1479.2 174.0 +L 12639 Metabolite -3012 507.17 1489.8 232.0 +L 12644 Metabolite -3016 50 7.58 1537.5 186.0 +L12645 Metabolite -3017 50 7.61 1541.4 246.0 +L 12647 Metabolite -3019 507.74 1556.4 260.0 +L 12648 Metabolite -3020 50 7.81 1564.1 292.0 +L12650 Metabolite -3022 50 7.98 1584.9 142.0 +L 12656 Metabolite -3025 508.11 1600.3 274.0 +L 12658 Metabolite -3026 50 8.17 1606.1 274.0 +L12663 Metabolite -3030 50 8.62 1659.7 320.0 +L 12666 Metabolite -3033 508.88 1689.4 117.0 +L 12667 Metabolite -3034 50 8.92 1694.9 299.0 +L12673 Metabolite -3040 50 9.27 1735.7 274.0 +L 12726 Metabolite -3058 509.70 1786.9 335.0 +L 12742 Metabolite -3067 50 10.02 1824.2 132.0 +L12751 Metabolite -3073 50 10.17 1838.8 362.0 +L 12753 Metabolite -307450 10.22 1844.5 204.0 +L 12754 Metabolite -3075 50 10.36 1857.9 204.0 +L12756 Metabolite -3077 50 10.44 1866.2 308.0 +L 12757 Metabolite -307850 10.65 1887.0 203.0 +L 12767 Metabolite -3087 50 11.19 1942.0 174.0 +L12768 Metabolite -3088 50 11.23 1946.1 372.0 +L 12770 Metabolite -309050 11.31 1955.0 243.0 +L 12771 Metabolite -3091 50 11.41 1966.2 232.0 +L12773 Metabolite -3093 50 11.50 1975.6 204.0 +L 12774 Metabolite -309450 11.55 1980.6 299.0 +L 12777 Metabolite -3097 50 11.64 1990.4 204.0 +L12780 Metabolite -3098 50 11.75 2003.0 308.0 +L 12783 Metabolite -310150 11.93 2022.2 290.0 +L 12785 Metabolite -3103 50 12.09 2039.8 290.0 +L12789 Metabolite -3107 50 12.21 2053.2 204.0 +L 12790 Metabolite -310850 12.24 2056.5 246.0 +L 12791 Metabolite -3109 50 12.56 2092.6 202.0 +L12796 Metabolite -3114 50 12.79 2120.6 204.0 +L 16028 Metabolite -399850 5.22 1252.7 171.0 +L 16044 Metabolite -4005 50 6.53 1401.3 86.0 +L16060 Metabolite -4014 50 7.17 1474.9 252.0 +L 16070 Metabolite -4019 507.68 1534.5 174.0 +L 16071 Metabolite -4020 50 7.91 1561.5 220.0 +L16116 Metabolite -4051 50 11.56 1970.2 357.0 +L 16290 Metabolite -413350 4.35 1108.9 198.0 +L 16308 Metabolite -4147 50 10.07 1767.1 290.0 +L16496 Metabolite -4251 50 4.09 1130.7 217.0 +L 16506 Metabolite -4271 509.69 1777.4 419.0 +L 16653 Metabolite -4361 50 9.40 1706.2 232.0 +L16655 Metabolite -4362 50 10.02 1779.9 319.0 +L 16819 Metabolite -449650 6.76 1398.2 204.0 +L 16821 Metabolite -4498 50 7.06 1434.9 103.0 +L16831 Metabolite -4504 50 8.46 1597.1 244.0 +L 16843 Metabolite -4510 509.70 1740.1 254.0 +L 16848 Metabolite -4511 50 10.09 1788.4 206.0 +L16859 Metabolite -4516 50 11.00 1886.5 217.0 +L 16860 Metabolite -451750 11.06 1892.7 217.0 +L 16865 Metabolite -4522 50 12.26 2025.4 217.0 +L16983 Metabolite -4598 50 6.69 1392.2 170.0 +L 16984 Metabolite -4599 507.42 1471.1 113.0 +L 17064 Metabolite -4624 50 10.01 1779.1 342.0 +L17083 Metabolite -4634 50 11.00 1884.3 333.0 +L 17327 Metabolite -476750 8.77 1626.2 117.0 +L 17359 Metabolite -4791 50 10.29 1796.4 366.5 +L17390 Metabolite -4806 50 4.20 1122.8 105.0 +L 17614 Metabolite -4966 509.66 1749.4 218.0 +L 17627 Metabolite -4986 50 11.56 1956.4 204.0 +L17971 Metabolite -5210 50 8.47 1616.4 254.0 +L 17975 Metabolite -5211 508.77 1652.1 326.0 +L 17978 Metabolite -5213 50 8.97 1675.3 111.0 +L17987 Metabolite -5228 50 6.97 1442.5 181.0 +L 18118 Metabolite -5346 508.33 1573.0 202.0 +L 18122 Metabolite -5349 50 10.10 1782.2 312.0 +L18146 Metabolite -5366 50 12.49 2044.7 204.0 +L 18147 Metabolite -536750 12.77 2079.3 171.0 +L 18232 Metabolite -5403 50 5.92 1300.2 319.0 +L18273 Metabolite -5420 50 9.09 1669.0 417.0 +L 18384 Metabolite -5487 507.01 1426.3 204.0 +L 18388 Metabolite -5491 50 8.30 1575.9 129.0 +L18868 Metabolite -5847 50 12.35 2040.0 288.2 +L 18929 Metabolite -590750 8.69 1643.2 229.1 +L 19110 Metabolite -5978 50 7.52 1468.9 232.1 +L19362 Metabolite -6226 50 4.38 1137.4 154.0 +L 19363 Metabolite -6227 505.00 1210.5 196.1 +L 19364 Metabolite -6246 50 6.94 1428.2 160.1 +L19367 Metabolite -6266 50 9.15 1683.5 240.2 +L 19368 Metabolite -6267 509.32 1704.5 257.1 +L 19370 Metabolite -6268 50 9.91 1773.8 271.1 +L19372 Metabolite -6269 - 50 10.88 1880.9 217.1 +L retired for inositol-319374 Metabolite -6270 50 11.35 1929.6 320.2 +L 19377 Metabolite -627250 12.60 2069.6 131.0 +L 19383 Metabolite -6286 50 16.36 2553.7 311.3 +L19397 Metabolite -6326 50 7.66 1510.9 144.1 +L 19402 Metabolite -6346 508.00 1550.8 263.2 +L 19405 Metabolite -6347 50 8.16 1568.7 244.1 +L19414 Metabolite -6350 50 11.41 1937.2 169.0 +L 19490 Metabolite -648850 12.25 2021.7 204.1 +L 19494 Metabolite -6506 50 12.81 2084.7 204.1 +L19596 Metabolite -6647 50 9.13 1696.7 197.1 +L 19597 Metabolite -6648 509.17 1702.1 313.2 +L 19599 Metabolite -6649 50 11.47 1955.4 299.2 +L19623 Metabolite -6671 50 9.65 1738.4 229.0 +L 19955 Metabolite -6907 509.22 1686.9 337.1 +L 19961 Metabolite -6913 50 9.53 1726.0 171.0 +L19968 Metabolite -6930 50 10.32 1809.4 331.2 +L 19969 Metabolite -693150 10.35 1819.6 267.1 +L 20299 Metabolite -7266 50 7.82 1517.6 115.0 +L20950 Metabolite -7846 50 5.10 1208.1 145.1 +L 21011 Metabolite -7888 5015.96 2513.3 311.3 +L 21012 Metabolite -7889 50 16.83 2629.4 311.3 +L21013 Metabolite -7890 50 17.76 2752.2 129.0 +L 21415 Metabolite -820950 14.77 2338.0 456.5 +L 21421 Metabolite -8214 50 17.13 2646.6 311.2 +L21586 Metabolite -8359 50 7.14 1457.8 253.0 +L 21630 Metabolite -8402 5015.27 2424.0 283.1 +L 21631 Metabolite -8403 50 15.96 2516.6 309.2 +L22020 Metabolite -8749 - 50 9.74 1763.0 204.1 +L retired for fructose-422032 Metabolite -8766 50 12.22 2034.0 315.1 +L 22054 Metabolite -879250 17.61 2737.0 129.0 +L 22320 Metabolite -8889 50 8.62 1635.0 521.2 +L22480 Metabolite -8987 50 7.02 1449.7 160.1 +L 22494 Metabolite -8994 5010.76 1879.0 447.2 +L 22507 Metabolite -9010 - 50 12.98 2126.2 217.1 +Lretired for adenosine - 1 22548 Metabolite -9026 50 8.45 1600.0 156.0 +L22555 Metabolite -9027 50 8.50 1605.0 357.2 +L 22570 Metabolite -9033 509.61 1736.4 217.1 +L 22572 Metabolite -9034 50 9.63 1739.1 241.1 +L22577 Metabolite -9035 50 9.82 1760.7 285.1 +L 22600 Metabolite -9043 5011.75 1974.1 204.1 +L 22601 Metabolite -9044 50 13.38 2169.0 204.1 +L22602 Metabolite -9045 50 13.91 2239.0 450.3 +L 22609 Metabolite -904750 8.06 1574.0 243.2 +L 22649 Metabolite -9108 50 11.20 1896.0 156.0 +L22880 Metabolite -9286 50 8.77 1617.5 221.0 +L 22895 Metabolite -9299 5010.54 1827.5 305.1 +L 22993 Metabolite -9448 50 14.86 2352.5 343.2 +L24074 Metabolite -9706 50 4.39 1107.0 190.0 +L 24076 Metabolite -9726 504.91 1167.0 245.0 +L 24077 Metabolite -9727 50 5.24 1204.0 177.0 +L10737 Isobar 01 61 1.45 1481.0 225.0 −i 10743 Isobar 04 61 1.52 1567.0195.0 −i 10746 Isobar 06 61 2.13 2160.0 118.0 +i 10750 Isobar 08 6110.04 10116.0 138.0 +i 12459 Isobar 10 61 1.40 1527.0 147.0 +i 16233Isobar 13 61 1.40 1530.0 193.0 −i 16232 Isobar 17 61 1.49 1620.0 175.0+i 16235 Isobar 19 61 1.55 1700 199 −i 16244 Isobar 21 61 1.59 1620.0241.0 +i 16228 Isobar 22 61 1.55 1635.0 148.0 +i 16229 Isobar 24 61 1.431545.0 153.0 +i 16226 Isobar 28 61 1.46 1525.0 120.0 +i 18829 Isobar 4561 8.38 8475.0 166.2 +i 18882 Isobar 47 61 15.51 15700.0 498.4 −i 21404Isobar 48 61 1.50 1550.0 106.1 +i 21410 Isobar 52 61 1.55 1650.0 134.1+i 21418 Isobar 56 61 2.45 2850.0 130.1 +i 22258 Isobar 58 61 1.381620.0 164.0 +i 22259 Isobar 59 61 1.82 1700.0 189.1 +i 22261 Isobar 6061 4.26 4725.0 148.9 −i 22262 Isobar 61 61 9.30 9675.0 174.8 −i 22803Isobar 66 61 15.06 15500.0 450.2 +i 27773 Isobar 71 61 1.57 1700.0 206.9−i 32718 Metabolite - 200 2.8 2848 265.1 +i 01342_200 32735 Metabolite -200 4.26 4275 464.1 +i 01911_200 32596 Metabolite - 200 5.14 5158 286.2+i 02250_200 - retired for piperine 32672 Metabolite - 200 0.75 764129.2 +i 02546_200 32829 Metabolite - 200 0.82 826 144.2 +i 03653_200 -retired for stachydrine 32595 Metabolite - 200 5.19 5200 431.9 +i08893_200 32734 Metabolite - 200 4.14 4229 288.2 +i 10954_200 - retiredfor (+/−) octanoyl carnitine 32514 Metabolite - 11200 - 200 5.62 5637496.4 +i retired for 1-palmitoyl- sn-glycero-3- phosphocholine 32516Metabolite - 11202 - 200 5.8 5823 524.4 +i retired for 1-stearoyl-sn-glycero-3- phosphocholine 32517 Metabolite - 11203 - 200 5.65 5665522.4 +i retired for 1-Oleoyl- sn-glycero-3- phosphocholine 32518Metabolite - 11204 200 5.26 5263 229.2 +i 32519 Metabolite - 11205 - 2005.55 5558 520.4 +i retired for 1-linoleoyl GPC 32520 Metabolite - 11206200 0.59 575 138.8 +i 32578 Metabolite - 11261 200 3.69 3600 286.2 +i32631 Metabolite - 11314 200 0.64 634 243 +i 32632 Metabolite - 11315200 1.19 1210 130.2 +i 32644 Metabolite - 11327 200 5.16 5176 269.2 +i32652 Metabolite - 11335 200 0.97 991 229.2 +i 32654 Metabolite - 11337200 1 1020 160.2 +i 32671 Metabolite - 11354 200 0.76 770 146.2 +i 32738Metabolite - 11421 200 4.54 4575 314.2 +i 32786 Metabolite - 11469 2003.82 3874 239.1 +i 32793 Metabolite - 11476 200 4.52 4525 189.1 +i 32875Metabolite - 11558 200 5.64 5606 420.2 +i 32971 Metabolite - 11654 - 2002.53 2500 246.2 +i retired for isovaleryl- L-carnitine 33080Metabolite - 11735 200 2.51 2584 207.2 +i 33081 Metabolite - 11736 2002.58 2639 379.4 +i 33082 Metabolite - 11737 200 2.7 2747 235.2 +i 33132Metabolite - 11787 200 1.13 1126 148.1 +i 33135 Metabolite - 11790 2003.4 3472 823.3 +i 33138 Metabolite - 11793 200 3.57 3634 601.1 +i 33228Metabolite - 11883 200 5.54 5524 544.3 +i 33323 Metabolite - 11977 2003.21 3287 270.1 +i 33403 Metabolite - 12051 200 5.83 5739 456.4 +i 33408Metabolite - 12056 200 1.12 1129 156.2 +i 33531 Metabolite - 12116 2001.6 1640 286.1 +i 33587 Isobar: cis-9, cis-11, 201 6.13 5955 309.4 −itrans-11 eicosenoate 32747 Metabolite - 201 1.19 1176 117.2 −i01142_201 - retired for 2-hydroxy-3- methylbutyric acid 32588Metabolite - 201 4.25 4242 583.2 −i 01327_201 32609 Metabolite - 201 4.94887 369.2 −i 01345_201 - retired for epiandrosterone sulfate 32587Metabolite - 201 4.03 4025 267.2 −i 02249_201 32550 Metabolite - 2011.97 1958 189 −i 02272_201 32756 Metabolite - 201 3.35 3339 199.1 −i02276_201 32553 Metabolite - 201 2.2 2199 173.1 −i 03832_201 32557Metabolite - 201 2.69 2684 203.1 −i 06126_201 32753 Metabolite - 2012.62 2613 153.1 −i 09789_201 32548 Metabolite - 11231 201 1.47 1471 330−i 32561 Metabolite - 11244 201 3.78 3771 224.2 −i 32564 Metabolite -11247 201 3.94 3932 213.1 −i 32616 Metabolite - 11299 201 4.9 4893 507.2−i 32619 Metabolite - 11302 201 5.01 4998 397.3 −i 32625 Metabolite -11308 201 5.15 5133 365.3 −i 32635 Metabolite - 11318 201 5.81 5699476.3 −i 32637 Metabolite - 11320 - 201 5.85 5740 593.9 −i retiredduplicate of X- 12528 32648 Metabolite - 11331 - 201 0.69 686 164.2 −iretired EDTA ions 32656 Metabolite - 11339 - 201 0.69 689 156.2 −iretired-EDTA ions 32682 Metabolite - 11365 - 201 5.61 5527 303.3 −iretired for arachidonic acid 32696 Metabolite - 11379 - 201 5.65 5566267.3 −i retired for cis-10- heptadecenoic acid 32732 Metabolite -11415 - 201 0.69 692 313.1 −i retired - EDTA ions 32748 Metabolite -11431 201 1.58 1575 330 −i 32754 Metabolite - 11437 201 2.89 2888 231 −i32757 Metabolite - 11440 201 3.58 3571 246.3 −i 32760 Metabolite - 11443201 3.92 3910 225.3 −i 32761 Metabolite - 11444 201 3.99 3983 541.2 −i32769 Metabolite - 11452 201 4.12 4109 352.1 −i 32792 Metabolite - 11475201 4.25 4240 383.2 −i 32795 Metabolite - 11478 201 4.3 4286 165.2 −i32807 Metabolite - 11490 201 4.77 4762 279.8 −i 32813 Metabolite - 11496201 5.58 5508 271.3 −i 32839 Metabolite - 11522 201 4.76 4754 313.2 −i32848 Metabolite - 11531 201 4.86 4850 391.3 −i 32850 Metabolite - 11533201 4.91 4904 243.2 −i 32855 Metabolite - 11538 201 4.93 4920 311.3 −i32877 Metabolite - 11560 201 5.29 5245 295.3 −i 32910 Metabolite - 11593201 0.79 790 189.2 −i 32970 Metabolite - 11653 201 5.82 5686 331.3 −i33172 Metabolite - 11827 201 1.56 1575 312.1 −i 33177 Metabolite - 11832201 1.95 1962 216.1 −i 33185 Metabolite - 11840 201 2.56 2574 135.2 −i33190 Metabolite - 11845 201 2.87 2891 615 −i 33194 Metabolite - 11849201 3.2 3229 266.2 −i 33198 Metabolite - 11853 201 3.59 3602 187.1 −i33210 Metabolite - 11865 201 5.04 5037 456.2 −i 33219 Metabolite -11874 - 201 5.23 5199 197.3 −i retired for cis-5- Dodecenoic acid 33225Metabolite - 11880 201 5.44 5378 537.4 −i 33226 Metabolite - 11881 2015.48 5414 380.3 −i 33227 Metabolite - 11882 - 201 5.52 5445 301.3 −iretired for cis- 5,8,11,14,17- eicosapentaenoic acid 33232 Metabolite -11887 - 201 5.85 5736 307.4 −i retired for cis-11,14- eicosadienoic acid33237 Metabolite - 11892 201 0.71 710 367.1 −i 33380 Metabolite - 12029201 0.68 683 329.1 −i 33388 Metabolite - 12037 - 201 5.88 5795 295.4 −iretired for cis-10- nonadecenoic acid 33389 Metabolite - 12038 201 5.825736 245.3 −i 33415 Metabolite - 12063 201 4.82 4822 427.2 −i 33416Metabolite - 12064 201 1 999 101.3 −i

While the invention has been described in detail and with reference tospecific embodiments thereof, it will be apparent to one skilled in theart that various changes and modifications can be made without departingfrom the spirit and scope of the invention.

1-61. (canceled)
 62. A method of monitoring the progression orregression of insulin resistance in a subject, the method comprising:analyzing a biological sample from a subject to determine a level oflinoleoyl LPC in the sample; and comparing the level of linoleoyl LPC inthe sample to insulin resistance progression and/or insulinresistance-regression reference levels of linoleoyl LPC in order tomonitor the progression or regression of insulin resistance in asubject.
 63. The method of claim 62, wherein the method furthercomprises analyzing the biological sample to determine the level(s) ofone or more additional biomarkers selected from the group consisting of,2-hydroxybutyrate, oleoyl LPC, oleate, linolenate, linoleate,glycerophosphorylcholine (GPC), and stearate; and comparing the level(s)of the one or more additional biomarkers in the sample to insulinresistance progression and/or insulin resistance-regression referencelevels of the one or more additional biomarkers in order to monitor theprogression or regression of insulin resistance in a subject.
 64. Amethod of monitoring the efficacy of insulin resistance treatment, themethod comprising: analyzing a first biological sample from a subject todetermine a level of linoleoyl LPC, the first sample obtained from thesubject at a first time point; treating the subject for insulinresistance; analyzing a second biological sample from the subject todetermine the level of linoleoyl LPC, the second sample obtained fromthe subject at a second time point after treatment; comparing the levellinoleoyl LPC in the first sample to the level of linoleoyl LPC in thesecond sample to assess the efficacy of the treatment for treatinginsulin resistance.
 65. The method of claim 64, wherein the treatmentcomprises administering a therapeutic agent to the subject.
 66. Themethod of claim 65, wherein the therapeutic agent is an insulinsensitizer.
 67. The method of claim 66, wherein the insulin sensitizeris a thiazolidinedione.
 68. The method of claim 64, wherein thetreatment comprises a lifestyle modification of the subject.
 69. Themethod of claim 68, wherein the lifestyle modification includes amodification of the nutrition, diet, or exercise routine of the subject.70. The method of claim 64, wherein the method further comprisesanalyzing the first and second biological samples to determine thelevel(s) of one or more additional biomarkers selected from the groupconsisting of 2-hydroxybutyrate, oleoyl LPC, oleate, linolenate,linoleate, glycerophosphorylcholine (GPC), and stearate; and comparingthe level(s) of the one or more additional biomarkers in the firstsample to the level(s) of the one or more additional biomarkers in thesecond sample to assess the efficacy of the treatment for treatinginsulin resistance.
 71. The method of claim 64, wherein the methodfurther comprises determining the subject's measurements of fastingplasma insulin, fasting plasma glucose, fasting plasma pro-insulin,fasting free fatty acids, HDL-cholesterol, LDL-cholesterol, C-peptide,adiponectin, peptide YY, hemoglobin A1C, waist circumference, bodyweight, or body mass index.
 72. A method of determining insulinsensitivity in a subject, the method comprising: predicting a glucosedisposal rate in a subject by analyzing a biological sample from asubject to determine a level of linoleoyl LPC in the sample; andcomparing the level of linoleoyl LPC in the sample to glucose disposalreference levels of linoleoyl LPC in order to determine insulinsensitivity in the subject.
 73. The method of claim 71, wherein themethod further comprises analyzing the biological sample to determinethe level(s) of one or more additional biomarkers selected from thegroup consisting of, 2-hydroxybutyrate, oleoyl LPC, oleate, linolenate,linoleate, glycerophosphorylcholine (GPC), and stearate; and comparingthe level(s) of the one or more additional biomarkers in the sample toglucose disposal reference levels of the one or more additionalbiomarkers in order to determine insulin sensitivity in the subject. 74.The method of claim 71, wherein the method further comprises determiningthe subject's measurements of fasting plasma insulin, fasting plasmaglucose, fasting plasma pro-insulin, fasting free fatty acids,HDL-cholesterol, LDL-cholesterol, C-peptide, adiponectin, peptide YY,hemoglobin A1C, waist circumference, body weight, or body mass index.75. The method of claim 74, wherein the method further comprisesanalyzing the subject and a biological sample from the subject using amathematical model comprising linoleoyl LPC and one or more additionalbiomarkers or measurements selected from the group consisting of,2-hydroxybutyrate, oleoyl LPC, oleate, linolenate, linoleate,glycerophosphorylcholine (GPC), stearate, fasting plasma insulin,fasting plasma glucose, fasting plasma pro-insulin, fasting free fattyacids, HDL-cholesterol, LDL-cholesterol, C-peptide, adiponectin, peptideYY, hemoglobin A1C, waist circumference, body weight, and body massindex.
 76. The method of claim 71, wherein the method further comprisesanalyzing the biological sample to determine the level of2-hydroxybutyrate, oleoyl LPC, oleate, linolenate, linoleate,glycerophosphorylcholine (GPC), and stearate.
 77. The method of claim71, wherein the biological sample is a plasma sample.
 78. A method ofdetermining an insulin resistance score in a subject, the methodcomprising: analyzing a biological sample from a subject to determinethe level(s) of one or more biomarkers in the sample, wherein the one ormore biomarkers are selected from the group consisting of linoleoyl LPC,2-hydroxybutyrate, oleoyl LPC, oleate, linolenate, linoleate,glycerophosphorylcholine (GPC), stearate, and combinations thereof; andcomparing the level(s) of the one or more biomarkers in the sample toinsulin resistance reference levels of the one or more biomarkers inorder to determine an insulin resistance score for the subject.
 79. Themethod of claim 78, wherein the insulin resistance score is used tomonitor the progression or regression of insulin resistance in thesubject.
 80. The method of claim 78, wherein the insulin resistancescore is used to monitor a course of treatment in the subject.
 81. Themethod of claim 78, wherein the method further comprises determining thesubject's measurements of fasting plasma insulin, fasting plasmaproinsulin, fasting plasma glucose, fasting free fatty acids,HDL-cholesterol, LDL-cholesterol, C-peptide, adiponectin, peptide YY,hemoglobin A1C, waist circumference, body weight, or body mass index.